{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimizer tweaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "from exp.nb_08 import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imagenette data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We grab the data from the previous notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=3917) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = datasets.untar_data(datasets.URLs.IMAGENETTE_160)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfms = [make_rgb, ResizeFixed(128), to_byte_tensor, to_float_tensor]\n",
    "bs=128\n",
    "\n",
    "il = ImageList.from_files(path, tfms=tfms)\n",
    "sd = SplitData.split_by_func(il, partial(grandparent_splitter, valid_name='val'))\n",
    "ll = label_by_func(sd, parent_labeler, proc_y=CategoryProcessor())\n",
    "data = ll.to_databunch(bs, c_in=3, c_out=10, num_workers=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then a model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "nfs = [32,64,128,256]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbfs = [partial(AvgStatsCallback,accuracy), CudaCallback,\n",
    "        partial(BatchTransformXCallback, norm_imagenette)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the baseline of training with vanilla SGD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn,run = get_learn_run(nfs, data, 0.4, conv_layer, cbs=cbfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [1.7649338173278268, tensor(0.3866, device='cuda:0')]\n",
      "valid: [1.69968408203125, tensor(0.4180, device='cuda:0')]\n"
     ]
    }
   ],
   "source": [
    "run.fit(1, learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Refining the optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In PyTorch, the base optimizer in `torch.optim` is just a dictionary that stores the hyper-parameters and references to the parameters of the model we want to train in parameter groups (different groups can have different learning rates/momentum/weight decay... which is what lets us do discriminative learning rates).\n",
    "\n",
    "It contains a method `step` that will update our parameters with the gradients and a method `zero_grad` to detach and zero the gradients of all our parameters.\n",
    "\n",
    "We build the equivalent from scratch, only ours will be more flexible. In our implementation, the step function loops over all the parameters to execute the step using stepper functions that we have to provide when initializing the optimizer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=4074)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Optimizer():\n",
    "    def __init__(self, params, steppers, **defaults):\n",
    "        # might be a generator\n",
    "        self.param_groups = list(params)\n",
    "        # ensure params is a list of lists\n",
    "        if not isinstance(self.param_groups[0], list): self.param_groups = [self.param_groups]\n",
    "        self.hypers = [{**defaults} for p in self.param_groups]\n",
    "        self.steppers = listify(steppers)\n",
    "\n",
    "    def grad_params(self):\n",
    "        return [(p,hyper) for pg,hyper in zip(self.param_groups,self.hypers)\n",
    "            for p in pg if p.grad is not None]\n",
    "\n",
    "    def zero_grad(self):\n",
    "        for p,hyper in self.grad_params():\n",
    "            p.grad.detach_()\n",
    "            p.grad.zero_()\n",
    "\n",
    "    def step(self):\n",
    "        for p,hyper in self.grad_params(): compose(p, self.steppers, **hyper)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do basic SGD, this what a step looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def sgd_step(p, lr, **kwargs):\n",
    "    p.data.add_(-lr, p.grad.data)\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt_func = partial(Optimizer, steppers=[sgd_step])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have changed the optimizer, we will need to adjust the callbacks that were using properties from the PyTorch optimizer: in particular the hyper-parameters are in the list of dictionaries `opt.hypers` (PyTorch has everything in the the list of param groups)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class Recorder(Callback):\n",
    "    def begin_fit(self): self.lrs,self.losses = [],[]\n",
    "\n",
    "    def after_batch(self):\n",
    "        if not self.in_train: return\n",
    "        self.lrs.append(self.opt.hypers[-1]['lr'])\n",
    "        self.losses.append(self.loss.detach().cpu())        \n",
    "\n",
    "    def plot_lr  (self): plt.plot(self.lrs)\n",
    "    def plot_loss(self): plt.plot(self.losses)\n",
    "        \n",
    "    def plot(self, skip_last=0):\n",
    "        losses = [o.item() for o in self.losses]\n",
    "        n = len(losses)-skip_last\n",
    "        plt.xscale('log')\n",
    "        plt.plot(self.lrs[:n], losses[:n])\n",
    "\n",
    "class ParamScheduler(Callback):\n",
    "    _order=1\n",
    "    def __init__(self, pname, sched_funcs):\n",
    "        self.pname,self.sched_funcs = pname,listify(sched_funcs)\n",
    "\n",
    "    def begin_batch(self): \n",
    "        if not self.in_train: return\n",
    "        fs = self.sched_funcs\n",
    "        if len(fs)==1: fs = fs*len(self.opt.param_groups)\n",
    "        pos = self.n_epochs/self.epochs\n",
    "        for f,h in zip(fs,self.opt.hypers): h[self.pname] = f(pos)\n",
    "            \n",
    "class LR_Find(Callback):\n",
    "    _order=1\n",
    "    def __init__(self, max_iter=100, min_lr=1e-6, max_lr=10):\n",
    "        self.max_iter,self.min_lr,self.max_lr = max_iter,min_lr,max_lr\n",
    "        self.best_loss = 1e9\n",
    "        \n",
    "    def begin_batch(self): \n",
    "        if not self.in_train: return\n",
    "        pos = self.n_iter/self.max_iter\n",
    "        lr = self.min_lr * (self.max_lr/self.min_lr) ** pos\n",
    "        for pg in self.opt.hypers: pg['lr'] = lr\n",
    "            \n",
    "    def after_step(self):\n",
    "        if self.n_iter>=self.max_iter or self.loss>self.best_loss*10:\n",
    "            raise CancelTrainException()\n",
    "        if self.loss < self.best_loss: self.best_loss = self.loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So let's check we didn't break anything and that recorder and param scheduler work properly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sched = combine_scheds([0.3, 0.7], [sched_cos(0.3, 0.6), sched_cos(0.6, 0.2)]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbfs = [partial(AvgStatsCallback,accuracy),\n",
    "        CudaCallback, Recorder,\n",
    "        partial(ParamScheduler, 'lr', sched)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn,run = get_learn_run(nfs, data, 0.4, conv_layer, cbs=cbfs, opt_func=opt_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [1.7421688685144252, tensor(0.4011, device='cuda:0')]\n",
      "valid: [1.589141357421875, tensor(0.4820, device='cuda:0')]\n",
      "CPU times: user 4.72 s, sys: 2.29 s, total: 7.01 s\n",
      "Wall time: 11.2 s\n"
     ]
    }
   ],
   "source": [
    "%time run.fit(1, learn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run.recorder.plot_loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "run.recorder.plot_lr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight decay"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=4623)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By letting our model learn high parameters, it might fit all the data points in the training set with an over-complex function that has very sharp changes, which will lead to overfitting.\n",
    "\n",
    "<img src=\"images/overfit.png\" alt=\"Fitting vs over-fitting\" width=\"600\">\n",
    "\n",
    "Weight decay comes from the idea of L2 regularization, which consists in adding to your loss function the sum of all the weights squared. Why do that? Because when we compute the gradients, it will add a contribution to them that will encourage the weights to be as small as possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Limiting our weights from growing too much is going to hinder the training of the model, but it will yield to a state where it generalizes better. Going back to the theory a little bit, weight decay (or just `wd`) is a parameter that controls that sum of squares we add to our loss:\n",
    "``` python\n",
    "loss_with_wd = loss + (wd/2) * (weights**2).sum()\n",
    "```\n",
    "\n",
    "In practice though, it would be very inefficient (and maybe numerically unstable) to compute that big sum and add it to the loss. If you remember a little bit of high school math, the derivative of `p**2` with respect to `p` is `2*p`. So adding that big sum to our loss is exactly the same as doing:\n",
    "``` python\n",
    "weight.grad += wd * weight\n",
    "```\n",
    "\n",
    "for every weight in our model, which in the case of vanilla SGD is equivalent to updating the parameters with:\n",
    "``` python\n",
    "weight = weight - lr*(weight.grad + wd*weight)\n",
    "```\n",
    "\n",
    "This technique is called \"weight decay\", as each weight is decayed by a factor `lr * wd`, as it's shown in this last formula.\n",
    "\n",
    "This only works for standard SGD, as we have seen that with momentum, RMSProp and Adam, the update has some additional formulas around the gradient. In those cases, the formula that comes from L2 regularization:\n",
    "``` python\n",
    "weight.grad += wd * weight\n",
    "```\n",
    "is different than weight decay\n",
    "``` python\n",
    "new_weight = weight - lr * weight.grad - lr * wd * weight\n",
    "```\n",
    "\n",
    "Most libraries use the first one, but as it was pointed out in [Decoupled Weight Regularization](https://arxiv.org/pdf/1711.05101.pdf) by Ilya Loshchilov and Frank Hutter, it is better to use the second one with the Adam optimizer, which is why fastai made it its default."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weight decay is subtracting `lr*wd*weight` from the weights. We need this function to have an attribute `_defaults` so that we are sure there is an hyper-parameter of the same name in our `Optimizer`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def weight_decay(p, lr, wd, **kwargs):\n",
    "    p.data.mul_(1 - lr*wd)\n",
    "    return p\n",
    "weight_decay._defaults = dict(wd=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "L2 regularization is adding `wd*weight` to the gradients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def l2_reg(p, lr, wd, **kwargs):\n",
    "    p.grad.data.add_(wd, p.data)\n",
    "    return p\n",
    "l2_reg._defaults = dict(wd=0.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's allow steppers to add to our `defaults` (which are the default values of all the hyper-parameters). This helper function adds in `dest` the key/values it finds while going through `os` and applying `f` when they was no `key` of the same name."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def maybe_update(os, dest, f):\n",
    "    for o in os:\n",
    "        for k,v in f(o).items():\n",
    "            if k not in dest: dest[k] = v\n",
    "\n",
    "def get_defaults(d): return getattr(d,'_defaults',{})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same as before, we just take the default values of the steppers when none are provided in the kwargs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class Optimizer():\n",
    "    def __init__(self, params, steppers, **defaults):\n",
    "        self.steppers = listify(steppers)\n",
    "        maybe_update(self.steppers, defaults, get_defaults)\n",
    "        # might be a generator\n",
    "        self.param_groups = list(params)\n",
    "        # ensure params is a list of lists\n",
    "        if not isinstance(self.param_groups[0], list): self.param_groups = [self.param_groups]\n",
    "        self.hypers = [{**defaults} for p in self.param_groups]\n",
    "\n",
    "    def grad_params(self):\n",
    "        return [(p,hyper) for pg,hyper in zip(self.param_groups,self.hypers)\n",
    "            for p in pg if p.grad is not None]\n",
    "\n",
    "    def zero_grad(self):\n",
    "        for p,hyper in self.grad_params():\n",
    "            p.grad.detach_()\n",
    "            p.grad.zero_()\n",
    "\n",
    "    def step(self):\n",
    "        for p,hyper in self.grad_params(): compose(p, self.steppers, **hyper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export \n",
    "sgd_opt = partial(Optimizer, steppers=[weight_decay, sgd_step])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn,run = get_learn_run(nfs, data, 0.4, conv_layer, cbs=cbfs, opt_func=sgd_opt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before trying to train, let's check the behavior works as intended: when we don't provide a value for `wd`, we pull the corresponding default from `weight_decay`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = learn.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = sgd_opt(model.parameters(), lr=0.1)\n",
    "test_eq(opt.hypers[0]['wd'], 0.)\n",
    "test_eq(opt.hypers[0]['lr'], 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But if we provide a value, it overrides the default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "opt = sgd_opt(model.parameters(), lr=0.1, wd=1e-4)\n",
    "test_eq(opt.hypers[0]['wd'], 1e-4)\n",
    "test_eq(opt.hypers[0]['lr'], 0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's fit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cbfs = [partial(AvgStatsCallback,accuracy), CudaCallback]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn,run = get_learn_run(nfs, data, 0.3, conv_layer, cbs=cbfs, opt_func=partial(sgd_opt, wd=0.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [1.8518677231173415, tensor(0.3537, device='cuda:0')]\n",
      "valid: [1.947130615234375, tensor(0.3100, device='cuda:0')]\n"
     ]
    }
   ],
   "source": [
    "run.fit(1, learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is already better than the baseline!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With momentum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=4872)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Momentum requires to add some state. We need to save the moving average of the gradients to be able to do the step and store this inside the optimizer state. To do this, we introduce statistics. Statistics are object with two methods:\n",
    "- `init_state`, that returns the initial state (a tensor of 0. for the moving average of gradients)\n",
    "- `update`, that updates the state with the new gradient value\n",
    "\n",
    "We also read the `_defaults` values of those objects, to allow them to provide default values to hyper-parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class StatefulOptimizer(Optimizer):\n",
    "    def __init__(self, params, steppers, stats=None, **defaults): \n",
    "        self.stats = listify(stats)\n",
    "        maybe_update(self.stats, defaults, get_defaults)\n",
    "        super().__init__(params, steppers, **defaults)\n",
    "        self.state = {}\n",
    "        \n",
    "    def step(self):\n",
    "        for p,hyper in self.grad_params():\n",
    "            if p not in self.state:\n",
    "                #Create a state for p and call all the statistics to initialize it.\n",
    "                self.state[p] = {}\n",
    "                maybe_update(self.stats, self.state[p], lambda o: o.init_state(p))\n",
    "            state = self.state[p]\n",
    "            for stat in self.stats: state = stat.update(p, state, **hyper)\n",
    "            compose(p, self.steppers, **state, **hyper)\n",
    "            self.state[p] = state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class Stat():\n",
    "    _defaults = {}\n",
    "    def init_state(self, p): raise NotImplementedError\n",
    "    def update(self, p, state, **kwargs): raise NotImplementedError    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example of `Stat`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class AverageGrad(Stat):\n",
    "    _defaults = dict(mom=0.9)\n",
    "\n",
    "    def init_state(self, p): return {'grad_avg': torch.zeros_like(p.grad.data)}\n",
    "    def update(self, p, state, mom, **kwargs):\n",
    "        state['grad_avg'].mul_(mom).add_(p.grad.data)\n",
    "        return state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we add the momentum step (instead of using the gradients to perform the step, we use the average)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def momentum_step(p, lr, grad_avg, **kwargs):\n",
    "    p.data.add_(-lr, grad_avg)\n",
    "    return p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd_mom_opt = partial(StatefulOptimizer, steppers=[momentum_step,weight_decay],\n",
    "                  stats=AverageGrad(), wd=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn,run = get_learn_run(nfs, data, 0.3, conv_layer, cbs=cbfs, opt_func=sgd_mom_opt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [1.7929072509500543, tensor(0.3878, device='cuda:0')]\n",
      "valid: [1.6677803955078125, tensor(0.3960, device='cuda:0')]\n"
     ]
    }
   ],
   "source": [
    "run.fit(1, learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=5115) for discussion about weight decay interaction with batch normalisation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Momentum experiments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What does momentum do to the gradients exactly? Let's do some plots to find out!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=5487)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.linspace(-4, 4, 200)\n",
    "y = torch.randn(200) + 0.3\n",
    "betas = [0.5, 0.7, 0.9, 0.99]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_mom(f):\n",
    "    _,axs = plt.subplots(2,2, figsize=(12,8))\n",
    "    for beta,ax in zip(betas, axs.flatten()):\n",
    "        ax.plot(y, linestyle='None', marker='.')\n",
    "        avg,res = None,[]\n",
    "        for i,yi in enumerate(y):\n",
    "            avg,p = f(avg, beta, yi, i)\n",
    "            res.append(p)\n",
    "        ax.plot(res, color='red')\n",
    "        ax.set_title(f'beta={beta}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the regular momentum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def mom1(avg, beta, yi, i): \n",
    "    if avg is None: avg=yi\n",
    "    res = beta*avg + yi\n",
    "    return res,res\n",
    "plot_mom(mom1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, with a too high value, it may go way too high with no way to change its course.\n",
    "\n",
    "Another way to smooth noisy data is to do an exponentially weighted moving average. In this case, there is a dampening of (1-beta) in front of the new value, which is less trusted than the current average. We'll define `lin_comb` (*linear combination*) to make this easier (note that in the lesson this was named `ewma`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def lin_comb(v1, v2, beta): return beta*v1 + (1-beta)*v2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def mom2(avg, beta, yi, i):\n",
    "    if avg is None: avg=yi\n",
    "    avg = lin_comb(avg, yi, beta)\n",
    "    return avg, avg\n",
    "plot_mom(mom2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see it gets to a zero-constant when the data is purely random. If the data has a certain shape, it will get that shape (with some delay for high beta)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = 1 - (x/3) ** 2 + torch.randn(200) * 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y[0]=0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xR/7za8C5mnXZeP+TfHS6Egu/f5LWB7YyMzrC8GjvjSI4JQXi4jIEeGZ0vMMLAPIfsygI+UQ0WyhwbB7fvrtWU/fcSf6962Fa/fY9REUZoSB164LJdHW7HSu5dfs/rH/wGdpFRFw90I03Xv174ECaA80BfO+Cj94xSqX27AlDh3KsWThLbujPzgo1qHj+DLdvX84Vd0821GrML+Nn8+DNoSz+9xQtHrqL/tuW8XnHQXy2ZA/PdgvBPGQIREdjPntWNFtwicIyquOBenav/YEj6cu7ZFn+j6MDaK0nABMAIiIinNfBEUoWR47ADTfAnDkQGprr5vYZ1pFB1Tl3IJq4qrU5XKMe/iHVjI3eeAPmzoUaNWDwYCKDqlN+/yaS3NzZENyaYfZehpAQI1nGPf1SmDjx6rrQUPj9d6Ps0q+/wuHD1B/5FN3Mt9K8gg8jV/7ELt9AJkTewcVKVdm7fitVgbjv1nLWqwIRh3fwhiWe1DQrvoumEgxw/LgRSwjMio4nOdXK75sOMTCiHgNa+zsVZcksFwoZ0WyhQMiq2Z7uJu77dz4HfOrAm2/CzMnw8cdG8uATT8BXXxEZVB1vZeXFlT+xvVYw7q+/nut5AMMwj4w0utz+9RfWjZuotm49AyZPZGlIJKN2r8UrLYWfW/UGLy9atG0GVasSGaSZ1rI7H8/5iDaHtrFGNcf774V8GxVlHHfzZqjdWDRbyJXCMqrnAk8qpX7FSHA5q7U+qpRaBLxrl+jSA3i1kMYkFAfWrYN9+2DNmgyjOpsY3XknBAdjefzljLALN5Pii6aKXvuj2dL/fqYMiyTk0jHjmPPmGc9ffgmDB2MO9CH0xFaOt4pkwoibsgucu5PLIDQUJkyAw4dh1Sq297mbqBNXeHjjHADWtLuFg2PH0T9ZZxLOXx7pwJm/I+hyNIbUNCtWq6ZzzCaS3T3xTE2GgweJOmyi8851dDiwmcTylZlwZQAzo+MdZo1LZrlQBIhmCy7jzIC0D5VzMylG9wtjZqfKhL6zg0OvjaZVs3oQHm7MLLq5wbhx0KUL5jvv5M/qhwg4e5yYjz8h1JYDkxtKwUcfQefOcOQI64e9yPNeYXzw1+f03LWazV1vY23fezkRGMI0O4PYHOiDev8ZLi8ax3cz36ZcajIpbu4kVvHF5+wpWL+eqBa+cCWJVkf34p1yhdmXL4tmC9koqJJ60zC8F75KqXiM7HAPAK31N8B8jNJMMRjlmR5MX3daKfU2sDH9UKNtCTBCGcHWMOXAAcCJGC1fDnPmsCPsZtrt3oj/2eOkKROB38/nSmUfwr/5CKr5wAUP41hWqxFPFxUFmzZB5cqUi91LwDNPEpCDsGX7YTCnR16//z6cO0f5Ht35+ERt3NPSqJp0gUtjxzGoY8NsxzEH+sCt3eGNNxi56meGbJxLpeTLzArtyoDty9mzYRsDF89nxMzvuezuRfnUJA5Vqcnc5jc57E52vVrwCmUX0WyhoMjJgLRvupVq1bwx+z9mrRmH1dOLes89YRygQwewWGDmTHj3XRg+HEJDaTB+LLRsSfDDg3I9fybd7tTJqEH97794vfgcib9sYejdb+OBlTf7t+SZdo5DkVo3qcvBV99k99Q/2F+lNnXOnuC38Fv4YP7neC1bRZ8rcM/4N6l+6SwAv4T34s2eI0SzhUwUVPWPHL/16RnkI5ysmwRMKohxCCWQnUYZJJtRnVWMNuw+ivm08Zt9z3ODuO/UyUy7PzHgfzx8XmGuBlSsCD4+Rrml8ePh3nth7FgICgKTyfB4O8HhD0OHDtCypeE9AYLu7M0bh1IZZX0cq9Z4LtxDI/9qjsWyY0cAHlsznf9C2zG7dktW1m/FgO3LObtoKY1++Z6T99zHF/2f4bkHb+KGA//yV8tuHDlzGUtcIua1CyE+PlO5KcksFwoK0WyhoMjJgLQ13Uq1aiolXeSxqBm0jFrIV50H0/6CCXN1YORIQy/79YOmTQ3NbdXK6Hq4ZInhfXaCU4N+yhRITqZ1uXKM6pvKqDnbSNEmRs/bTuNalZwauAGjRnLygUf4acke1sScwqphc51G3LhyOdUWzOVCi1b80m0QVef9wcCtS/imyxB8vD2z1a8WzS67SEdFodBwOEVo81Snt/rOKkYdK6d3KwwMxOPQITY/8TKPmkIxpaXhlZrMwep1CbX3AgQEQEoK9OoFjz9uGNU+PkbSSpZscHuc/jC8/DIMHmzEXtepQ+LuGKxa5+6BaNsWPDwgLIzUabOY9ssWdFIyacpE81ULAPAb+Rxvt2rF6V+6023VSgCmbTjIzOh4Ns3/lIrb/4MnjHra0wY3Z9e6rTTqJtOIgiAUDk7jgi9fNnoDmEw5GpDmQB8+D/OAd97mll1rcNdWZobdxKcdBqFs2unvD3ffbezQqJExM/jss0b/AfuERAc41W2TCcqVAyDxUrJrmm035me7N2LjgdOkpFrZ6t+EXrvXQrlyVJwzi3vr1mVbz454dmvHz7tn8cUHW5kX0oE6V87ys99xAl57QaqBlGHEqBYKBYcehYCq2cI/sopRi1NGSbuY/73DquoNaRHRhGeOnWfUnG2GtzirF+Duu+HSJfD0ZMvABwn97HPcExLgoYdyHJ/TH4aBA2HMGMMoz2m7rFSoYJR9atyY1jVqMGVYeaJiE0ibUptycfuN9c2NOtbV+veFP2fRdddasGqWNG6Px+5dkJRkeGr69aPVV+/RatIkOHXq2v4BgiAIecCpFzgtzXAyPP44vPaaYwPSaoUzZ9gz/x96DBuCdvdg3a338m3lZkTVbYaHh5tD7bTEJRLVog+dLL1p2Tok1zG6osfX4jW2f0/dzHfC0knw1FNGdRIgrGsbuOsuGkyfzqeA6vMcrY7uISD6Lwj2h3vuwezriXntRug8MPcPWyg1KGOWr2QRERGhN23aVNTDEPLAuOUxjF28G6sGNwXP92hM5/JXaNG+OSl+NfE4mV4Vw8sr845//QV9+3L30E/YWKtRhrgDOXoBbD8IIxd+S6/dazhq2UbrkJo5jtFpss2+k0TFnSGyoW+mpgSOknJy9Ux07gyrV8NNNxlGNxhhHvWuFlq4e+gnTJ/8vPHioYfgiy+gdm2j1vWqVUbMoFBiUUpZtNYRuW9ZehDNLnk40uzIoOrELFzJ3Y/dbuScOPufvviiMUsI7PSrz7AhY/ji2V6Ac92+1uQ+R7qbdVm+NFtrmDoVbr/dcIbYuHyZ7SujqXzPnRyoWpumJ/fje/GM4XnftcuIDZ86FX74AYYOzfV9CMWXvGi2eKqFQiGrt8DH25NPv/iNH4A/azdnwMnjcPDg1WYsNo4fB+BoucqZpu9GdA3OUXBt04LvdH2Ij264j/7bTjg1qu2F1daS1n7dkMmbDKFfHpMh9Nf8g1C/vmFUd+hwdZm/P3TqxJXDRym3fx/jLkdfXT5vHgcat6S+rZX6hg1iVAuCcN1xpNlDJkYxKGo+ADo6GpWQANUdeH7XreOMf32+a3gDv7TsxYVylXLVbftQjqQUKzOj410uV+eo4khWLb5mzVYKhgzJvrx8eUJv6cjRwYPoPP5TY9mzz8Jnn5ESEIjH6QS0uztq8mQxqssQpqIegFA2sE2nPd+jMVOGRbL9yFkCThwEYEX9VsZG6SEgzJyZYUzbns9VqYabwuXpu8ig6ribFFaTG5c9yjHDEo8lLjHbdjZhHbt4N0MmRmXbxlHMniNc3Y7AQOPZ3qgGWLGCcrt3Qvny+P71h7Fs5Eg4cYKAl58lpro/hyv7cXr56lzfe05Y4hIZtzzG4WchCIJgw5FmJ6VYiTi0nRSTG0pro7Z0VrSGHTtIubEr33e+hwvelVzSbZtmg9FNqNhodi7UfuxB4w9vbxgzhr0/TGelbwizQ7vyRcd7YMUKozNjPhDdLjmIp1q4bljiEjM6UA1o7Y/5yC7M5RUWqvP7xoN8G7uJs14V+K9eM2OHAwfg6FGjSsfLLxsJK8ePQ6VKfP94lzwlfZgDfRgYUY+p6w+igbQ0xwkqOWWuW+ISOXzmMu5uJtLSco7HcxS353BqsUMHwwOd1ag2mYxH27aGCFerBk8+yeLjqZybO595jTsycNtSOm/amP3kLiK1UwVByIlsmp3u4bXEJfL7pkNorWkTv4PFTTrS82A0bkuWGHq9dq1hVIaHG5p95gx+bcOZ0s/1ZL2C0GwAH29PTEoBumA0OzeaN4d27Yy+Bt7eLA5szdgBr2PV4H/+JE+vnGJ4q0ePdu14WRDdLlmIUS1cFyxxiQyasI7kNCNm//dNh9j60+N4JV3GMvlv7l//B11jLYzp+hAdu7SEb9wMo3rDBuMAtli9Y8egZs1ragE7oLU/M6PjrymJxV7I3E2Ke9oG5Ng9K2uyDuBYCHv3hkOHHH5eUbEJDGjWitorVkCzZqAU1R8ZytOqCSmpVpolxtNn12o4ccLoFplHpHaqIAjOyKbZlnimPWLoVlRsAqlWTeCZo9S4mEhK5xtxO1IFFi40OtLedhv4+cGOHVfLpDZtmmfdzo9m297D6HnbSbNq3EyKUX1D86/ZOXxeGQb46tUZpf/sx3eqWk3O3diNKt99B6+/Dp6eLn8WNkS3SxZiVAsFhr3IRMUmkJJ2NQk26Nh+vOKNcI+7xo6k4vIlLA6J5OcOdzClTX0jUW/fPruDWYxpxOPHoWbOCYbOcKWskbNt7IUszaqpU7W8S54W2zbjlse4LIT2Bvy2hMp8DdCsWcbnOapvKImXkuneqj8s/d648ejbN8+fh9ROFQTBnpw02163IoOq4+mmGBH1OwDBd/WFhOZw111GhaSEBOPx77+ZjOq8kh/Nhqu6rQGtNYmXknM9X34125EBPqC1f4a3v0rHF4wSrzNmGOVZ84jodslCjGqhQMgqMqP6huLhpjK8HrfsW29s2KEDVZct5kK4mQNjvmFKaIAhRu3bw+LFhmca4MwZIw7t+PFrEmcbrnhKHG2TXyHLy/72BvzGWo1J8SrH4eCwbKLduF1XKF/e8A5dg1EttVMFQbCRm2bb65Y5oCrLzi+nzn9/c/SpFwi7qS1YI4xGLQsWGAnmcXHwyy9Gn4BKlTLKz+WVa9VsyJ9uX6tm2xvgWT/TAa39oUcPo/72Z58ZnR5NeUtlE90uWYhRLRQIWUUm8VIy04a3Z9OUuTRfs5hWx/414s5++gk+/5yKb73F8GrVrh5g8GCYNs2IJ27TBjZuNLzVx49D166F/n7yK2R52d9ezC9U9mHHmi2sSdQkL43JItrBRr3s2bONMnt5FGfbuESUBUFwptmZYqqjFsPdn0L9+tSZPh0eeojan39kHMBkgnfegVtvhZdeMm72p06FBg2gSZMcOyFeL/Kj29eq2fYGuNNQjZEjYdgweOMNo+/BNbwv0e2SgRjVQoHgSGTMgT6Y//wGoqIAiHr4eWJPeZDY/2kiba3FbfTocbXF+IMPwpYtsG4dnD59zeEf+SW/Qubq/lnFvGWgD6lxiXiuiM3uNenf3zCqN240blLsuKYkG0EQyiRONduWqH3gNKdffo2qx49gWr+ebbcOIun1DzHbG8t9+xrOj/BwCA6GuXMNR8j99xfRu8qfbl+rZtv2certfughWL8e3n3XiDsfORLatxfNLoVI8xehwMgmEFu3QosWHHvsGX7ae55fwnpwrlxFTArHiSCPPgoTJsB//xkidOYMxMTAN98Y665lDCUYh+8lMdFIUnz+efjgg4xtfLw9GT1vO0kpVtxMitH9whjcLqBo34DgEGn+IhQXcmqK8vGb3zPtx5d4tc8zLAuJ5KRXRTw93HJO4Fu2DO69Fz76yHFt5zyMoaTi9P2kpMCbb5Ly3US4cJFFk//kxW1JotklAGn+IhQJ2e7yv/sOPD3565bBfFP1JNb0+zeniSCvvmqUmwsNNbpXvf46APNPWKkZl5ir4Ja20kMOvSY+PkbSyyefcPRCCkdXRBPdtAsrQtpCaip1zieQ4F2ZUXO20bhWJZfff2n7YRMEIXeceWaj9p3ino3zOO9ZnjmNOnPZsxwaF6pP3HQTlrXbiNp/msgyqNmQg7fbwwPLoy/x3KVmzPruScIfHcT08lXQSrHBP5RRDBPNLgWIUS1cH2ytXfv3J7xVCJ6WBJJTrFgBk7PcGS2hAAAgAElEQVQmLvXrGzFnAP/7H7GVanLiky9560Qlzk2MylVwS0vpoZzE0hKXSPQjb9H79EXqjv+UXspE+9h/+V/Pp/hw/mdUSbrI38HtePTONzKaGeTWzn1mdDwzLPGkppWeHzZBEK6RU6e4/7WHqLRjNZMi+pFavjweSuVaqx/SjeTv17tsJJcVzbatmxUdz6FyPjxz60t8sOALLnh5UynpIsM3zub3lj2Iim0E5KzZAFPXH2TUnG1YtRbNLmYUiFGtlOoJfA64ARO11u9nWf8pYMs28wZqaK2rpq9LA7amrzuotb6tIMYkFDExMUaJpZtvzhR/5uPtSeKl5FzFxxzow4LmXRg7qDZWDW4uCG5pKD2Uk+fGti4pxcq7HZ/FP2wwFZMvMffH5/h29hgS/BsQZapI20Pb8HIjo62wsx84++PZgsBK8g+b4Dqi2YJTxo6l0oa1HHz7I5La9WFao1qAc0Mva1m+vBjJZUWzbf0OrBjdItfUD+empybzUIf6zFloYc2X99Nv9yp8vHvn6rm3xCUyas42UtOnfpNFs4sV+TaqlVJuwDjgZiAe2KiUmqu13mHbRmv9nN32TwGt7A5xWWsdnt9xCIWHS9NOtiYubdsCuSeAOBKmvApuSSg9lNtnl9OPkn0NVpSJQ1VrYQKm936Im7cs4/j02dRa/Q9VRj7N791qsOJScvZjxe8wss9nzcp8PEDheht4oeQiml32cDlU4PJlI2yvXz8CXn+Rx+1WOZvpylqWr7RpNuT8+bmi2VZNRqlCG01rVeLm0FrcHNqHI6vaMuzwRr6/mJTrTUlUbAJp1qvHMiklml2MKAhPdVsgRmsdC6CU+hXoB+xwsv0g4M0COK9QBLgcA7dhA1SoYHQGdAFHwjSia3CeBbc4lx5y5bPL6UYiMqg67iaVSZxNborRLfszKqwfnvPjmdmpNQBhh3eT1LlP9mN9871RV3bZMiJD22es7xS3hVbtQ+l82w3F9vMTCgzR7DJEnuKWp00zZhifesqlYzsqy1eaNBty//zyqtlGA3XYevgsQ9LDGv0fexAee4yuScf4Mpebksig6nh5mEhOsWJKT3Aszp9fWSPvhW6zUxew77scn74sG0qpQKABsMxucTml1CalVJRS6nZnJ1FKDU/fbtPJkycLYNjCteDI+AUgNRWeeAJ27cISl8jRJas4H9oC3NxcOq6PtycmpTLirX28PRm3PAaAEV2DS4VoOP3s7LB5bp7v0TibeJsDfRgYUQ9bQSsFhNauTGra1WOucPM1bmY2bHB8LFvXyjlzMta/3LEOP8x+h2envFcqPmchV0SzyxCu6A4YxuORz77mckhj6NIl1+Na4hI5fOYy7m4m3BS4mRRHzlwGSo9mQ+6fX1402wQEVvfGpDIn7HPHHeDmRrOV87MfS2v4809ISsp0vhduacz0R9tLxZBiRkEY1Y4qvDur03cPMENrnWa3LCC9VMlg4DOlVENHO2qtJ2itI7TWEX5+fvkbsXDN2O7K3bImG+7YAV9/zemXX+eBb1dTbfc2fjfVxhKXmOsxLXGJjJ63nTSrxqQUQ9vXZ/S87YxdvJshE6NcOkZJwOlnlwVzoI/TH6UBrf3x8jCO4eVh4u42AZmO2S6kBpjNGeE32Y4VG2s8//knWK2YA30YfmQjpiuXjTqq//57Xd67UKwQzS5DuKI7lrhEnvp8IbW2WphYOwLLwTM5HtPmvf11w0HQmm5Na4JSTNtwsFRpNrj2+bmq2Z4eJobf0DD78Xx9oXt3+PVXzN6pjIhbjdm/snGA5cvhttvgxRczn890GPP8Xw2jWyg2FET4RzxQz+61P3DEybb3ACPsF2itj6Q/xyql/sGI3dtXAOMSrgNOY+D27AGgyvy5RHo0wysthX9rhnDZhQQK+9herTXbj54rFRnhWSmI+EFHx2hcq1LmY7Zta3RcTEqCJUtg0yZ4M332ft8+8PODo0eN5W3bwqRJRuOGw4fh66+NWuFCaUY0uwzhiu5ExSbQeVcUJjSLGrbDlIvm2ntv06yayylpmWbMSotmw/XprptNs8FoYf7gg3DDDbBrlzHLe999sHSpsf6rr6B3b6OkamIiDBwIJ08aj1GjCvhdC9dKQRjVG4EQpVQD4DCGCA/OupFSqjHgA6yzW+YDXNJaJymlfIGOwIcFMCbhOuIwBm7vXgDcUlP4eva7nPfyJrpBC4a6kECRNSatV1htNh44XaIzwp1REPGDWY+R7Zjt28PHHxtdFz/4AFatgocfBm9vo6HOa68Znb0WLoTKlQ0P9dixsH07TJliiLenZ77GKBRrRLPLGLnpTmRQdZrGrCe+cg1i6jTMVXPLkmZDwXfXdXi82283mpzt2gVVq8L//R8MGmR4qlu1gkuXjJKzvXoZTpKEhKt/d+0KnTtf8/iEgiPfRrXWOlUp9SSwCKM80ySt9Xal1Ghgk9Z6bvqmg4BfdeYWjk2Bb5VSVoxQlPftM9CFEsSePVC7NoSHo6M389c73/LFzZ1cTlRx6U5ecI2uXUEpmDMno0U8M2dChw7G3xER0Lix4an29TWW9e8PdeoYXutdu6BFi6IZu3DdEc0WMti7Fzw9MV+6hPXgFrb2uYspj7TPVXNFs68DVavCY4/BhQvQr5/x+OwzI5Rv5EioVg1eesmYfRw/3jDAP/zQ0PDZs8WoLiZIm3LhmshWYqhTJ3B3NypLmEzg5VXUQyzbtG1reJ4vXYJy5aB1ayOjf9Agow38Rx8Z4ty9OyxeDEePsn1pFKE3d2D/p9/Q4FnX2sILeUfalAtFRSbdDqgK9erB8ePGLFb58rByJTRqVNTDFLSGbt0MLzXA339DSIjRIK1aNcPw3r8f6tThbJfu6H372Lc6Wm5mrhN50eyCSFQUSiGWuETGLY9xmHBiS1KxJRJOXX+QS9t3crJ2AJYTVxi39lCpSlQpkdx8s2FQe3jAc8/B2rXwzz/GuqAgDgY1g6NHSf1zHrRvj+XgGe5ankCSmztLf/sbS1xijt8BQRCKF7ldr/a6PWjCOj4Z/xccPsyFsJYcCQhh6+8LxKAuLihldCSuVcsIxevQAQuVOdYsHE6fhvvvhzp1sMQl8nn5RlSNP8DrH8wQ3S4GSJtyIRu5tUCNik0g8Oh+Bv27gE9uuJ+Ppkcx+MxpvjruzsTvoqTddXGge3cjbjoyEoYNM2KmJ0yAWrWwnErm4wMeTAPczyQS37glUbEJXLYq9voG0ujYfmZGxzMrOt7ldsOCIBQdrrStztqI5Mh8IwFuYNtH2F3NH8/Fx5gSkCjXeXGhVi1YtAj27MFyMokhE6MY6N+BN3ZvY8+QRwnD+J8ubtCGUXxN511RzIyOFN0uYsRTLWTCvgWqVV9tgWpP91N7mPHLSwyNnkefPasJPH0YgJiqdUhxoR6qcH2xxCXydXINUvxqwoABEBRkJCxqDQ0bEhWbwBbf+qQq4/LfWCskI/Fot199mpw8gAKXatsKglC0uKLZcDW50FZPMSJ+B2fKVWR31TpynRcDHHqYW7SAO+/MuCH6JbwnnZ6YzApdFTD+p6eq12KnX3167V0nul0MEE91GcOVNtm5tUBt/MNXJPtU4WJKRR6/tIcvzhrx0/G+dfFwN5GWVjozwEsC9t2/xg+bwOTbO2EGLLffT9r8NRyr3wgfb0+s5b3Z6xtAyKmD1L/lRlqlJx5dTmhLjW1LuSuwHDOjXW83LAjC9SO3NtmutK02B/owbVAYv+9KZEb0YdrE7yDavxnuHu6i2UWMo66NADOj440mX3WqpFdbgXNVqmX8n2wJo2ePDiDy+09wr6GZmYc28ULBI0Z1GcLVNtleHiasV5LxJI3/9Y/Ibnzv2YPnjTfgWaUKFaZN49Wbq5Dm5s47L9yOLldOMsCLEPsp3ku4EbX/NCjFoO+iSG49FADPP7fz1q2hnD06gHPH99OqqT9gCDQDboKv36PlmUP5rqktCEL+caVNtkttq9esodXNN9Pq2We5a+BDNBwTj9cjDzPtEbnOi5qsXRtnRsczY9OhjPbmnu4m3ro1lMRLydn+T+ZAH3j1Cfj+E5ovnsnyxAusCO9KyM0S+lEUiFFdhnDUbjXrRWe78/V66gma/DMf904/AQEZnpL2/pVovX+/UUUiIgImTKD6rOnw7LO0blw74xhC0ZC1fmxkUHWiYhNISbvqyUpJtRriPHFs9gO0bGk8b9yIuWtXzJeOQUDVQhq9IAhZyU23c2pOYtPtGzwu0Pz2fpCcDB98QPjq1QD4D7wV/wKonS/kj6y6rcChZo/oGuz4AA0bQps2MHo0tYF7qk2GNstA/q+FjhjVZYisF66Ptyfjlsdkv/Ot7gHL/oSUFLj9dr4Z8yOfXPIjNc1K47NHWGC1GuV9unUzqkv4+hqF6oUix/4H1sfbM+PZw01leD1ynBb084OwMKPcXr16MGSI0UjmhRcK8V0IgmDDXrfdTIojZy5jiUvMZljbXtsMaR9vT0bP2254uFdMJvTcOUwbNhj1j1etgnHjwGwuqrcl2GHTbftwD5c128YTTxitzD/4AN56y+hL8PbbRvUnpXLeVygwxKguQ2Q1uDIEN+uU4uzZcPEieyf/Rt1H7qPCjOkk93gCgLon441tGjWCihXhu++Mu+TKlYvoXQlZsf0f7aeM37otjG1HzqKAAa39c/ZMde9utCu3Wo3XL71E3BXF/La9aNuolni1BKEQsTe4ZljimbbhIDOj4x2G7/274xAffzidrdXrc8XLG6s2khfb7d/CsSYtqdO6tVGX/vBh4zoXihX2lTvypNkAQ4capfZMJrjlFs488DBVX3iBPTUb0GhI/8IYvoBU/yhzmAN9GNE1mMRLyc6zhH/+GerXZ7F/S1YEmbllzzp67VrN9Kmv0CwhztgmJMR4fuABo/GLUKzIOmWceCmZd/s3Z0z/5rmL8803Q1ISLFsGjz/OiZZtCHz9BTre348h362T+qeCUMiYA32oW7U8qWk5VHZYuJAWLRsy7ceX+GzOh5hSUuh8YDN+l88Senwf3HSTsV3TpmJQF0Pypdk2TIZJN/WwlQ7mx7noUY7oT74TzS5ExFNdRnEUewvA2bOwdCm88gqRwX5MbdqJXrvX8vm8j/FMS6VV6mnw8YHqklVcnHH6/7XDaUWBG280wnpSUtjZ8w5urdKHJypO4/nVU6iZcNRhLL4gCNeXHK9preG110ip68/PtVoydP0fLJ70BPVPH+FY05a4aSt1BvQpusELuZIvzc6yzag520g1ebC8YRu671rHb3tPiGYXEmJUl0VSUzEH+vC3aTPlf/uJg33uIGpvEADmXRuMaf+uXTEH+mB6+wlS//oMdzc3qFoFz6NHjBbY6bhykQuFT07JS5BLRYEKFeCGGyAujmUVA0jTe1ga3JbnV08h/OgeIoMGZDuWLaTIUXa6IAj5x1G+hG05y5ZBdDTlvvuO5t36E3/HcQJ2/Avt2lFr/XqjBXmkUaZNNLt4ki/NtsO+xOLCRu3pu2sV3RL3AY0zHUs0+/ogRnUpIE8iuXgx9O8PEyZQ7+N3SPUqh++XY/h5/xW+CO/GsnPLqGMyQbt2ALQKC4RPP4GaNWHNGvjss4zQD1cvcqFosE9eykpOFQUscYn899hoImpVILKeL14eMez1DeSKuyePeZ+maZbqAkMmRpGUYkUDJoV8FwTBBa7FuHWULzFlWCTmjz+G2rXhvvswe3nBmqVw5gy4uxvhHm3bgqenaHYxJz+abfsu2ZdYXBXchjRPLxqvWwIDe2dsK5p9/RCjuoSTF5G0xCVi+v43Wl26BPfeCx4e/PrtXPo80p/IuP+YE9qV03//Q9WmYXhXqnR1xxEjjOf69Q2julEjwLUSfULxxNlUY+bv01mmDAvI8J6kLmtJ0/jdmY4zKzqepBQrNc+d4v5/5/FNuzu5WL6ifBcEIQdc1W1HhnfsX8t47u+pLAppzxb/pixduIHWixZx9OmXqONlNOKifHnjAbBhA3h7A6LZJRnXNNv4Ltl7vN223mC0O0/Hptm2gn3yXShYCiRRUSnVUym1WykVo5R6xcH6oUqpk0qpzemPYXbrHlBK7U1/PFAQ4ylL2C6Q3NqSZtydrlvPAZ86XPKrxcZ7hmMKCcESEEq7Q9tQ1jQaxGxjdvmATIkNGe1TfYPghx/gkUeAqxe5m3Kx5I9QbLBNNT7fo3GmH3RnP7ojugZTsVN7sFggNRVLXCL/m72V6ZsO0eLIbv788VmeiJrBLXuj5LtQQhDdLhoscYl8tmRPru2kbZo9dvFuhkyMYur6gywb+R4DR9zJo+tn8d6ir3BXGq+pv6A13JvWNEO3M7W8DgqCWrUA0eySzLVotjnQx0g837EDDh9m6vqD/LrhIFprborZwKyfX6D/9uXyXShA8u2pVkq5AeOAm4F4YKNSaq7WekeWTadrrZ/Msm814E0gAtCAJX1fSVV1AUtcIr9vOpRxx+nm5vzCiIpNIDU5hbDj+5gVdhNjbn6UFGXCc952pvS6mfqfv03X/RYqpFxhY52mnEm/MLPfBffDXNu4mHOLAROKN46mGm0/uskpVpRS+Hh7Xl3Zti188QXbl65nyOpzJKVY8UhN4dN5Y0ly98SqTNzlm8o9Mo1Y7BHdLhocTb07M2jsjaXkFCufTVvD4m/fZWNAGNx1N20+foP/S9pJxy1/syawJXEV/TKMc2decNHskk2eNRsMoxrY/+scRiXUh7Q03vn7G+7dvAAA3zp+3DvsNfkuFBAF4aluC8RorWO11snAr0A/F/e9Bfhba306XZD/BnoWwJiykenOvZQQFZtAanpCggLuNDuvZenj7UnI6cNUTL7M1tqNSFGmjDvbfU1bA/DmkgkAbA4MyxB5R+1T7T/HTHfEQonHHOjDqL6hmEwKq9aMnrf96jXTpg0Ax5esJDnVMAoe2jSHoMQjvNXnSVJr16aNPivfhZJBsdft0qrZtmvHBHQM9nUa+uHj7YlJKUxo7t08nx+n/o8KyZd5vccTbLzlTmjQgHs+GUm9s8eZYu6TqYOqaHbZIUfNBmjRAvz8sP7xB3dFz2fuj89x7+YFfBt5B6fuGkLAnv8w16tSdG+glFEQRnVd4JDd6/j0ZVm5Qyn1n1JqhlKqXh73zRdbLHuoEd6MmE+/YcjEqFIj0rY71Can4ngmajp3tHL80VniEhk9bzuhR/YA0KjvTZmmABt27wiVKlHvzDHWDXuBsS/cmiG49tOFbibFDEt8xnRkafkchcwkXkrOaBqRaWo6OBiqVqX5kT14upsIOHucp9b+ys42XXj8vSfxDAmG2NiiHbzgKsVaty1xiaTe2AXvkS+UKq2x11NPDxPPdm/kNJZ69LztpFk1rY7uYfSi8biheaX3M8TVqk+7kJrw5ZcwaBC7f/2T5k8OzTDORbPLHk41G4za1d2703D137y7aBxu2spzt71Epc8+wbdPDzh3zggPEQqEgkhUdNT/Umd5/ScwTWudpJR6DPgRuMnFfY2TKDUcGA4QEBCQpwFuPHSWYWeOUfnyhVITkG9LYBnVN5S2I78geOVCSHgG6lfLtq3Nc9Hi6F7Oe5YnOaQRU3r5ZZ4CfOYZSE2l/bvvZmppaj9deOTMZaZtOCiJDaUcp/VSTSaIiMBv5xamjBmL3523Uc7TnaYzfoQAH2jQwKguI5QErrtu50ezo2IT6H7xDDW9TpU6rRnQ2j/XLnn2Hu0b921CK8WlxUsJStRMsWl2YB/o04fG2BdLE80ui+Ra43rkSKhTh52de7KsUiD3NvQ1vgfVOxjr166FsLDCH3gppCCM6nignt1rf+CI/QZaa/ssjO+AD+z27ZJl338cnURrPQGYABAREeHQ8HaGuZGRpFEuLaVUBOTbxzn7plwkKmqZseL338Fszra97YJrG7+dbXUaERnslz026+23nZ7Ptq0lLpGZ0fE5FqcXSj45xl22aQMffYR59XywrIFx48BmMAUFwZEjcPny1coDQnHluut2fjQ7Mqg6l73KUzHlSqnRmqz5KQNa+zvd1t5I6rLfwsVWEYSHNyTcxXOJZpctco2VDw+H8HCaAk3tlzdsCH5+sG4dDB9eiCMuvRSEUb0RCFFKNQAOA/cAg+03UErV1lofTX95G7Az/e9FwLtKKds3oAfwagGMKROtQgyjumuDKvQoBUlU9jFzN29fhSk52fAS/vYbvPdeJk8zGBfc77fUock7Bzj06NvUu8b3L0kuZQen9VLbtoXUVHjxRaPE4mOPXV3XoIHxHBcHTZpk21WaThQrirVumwN9OFe/FhfOXig19XPzUs7OprVbLHto8f4eeNy50yMnRLPLDjnVuHaKUtC+vdGDwgGi2Xkn30a11jpVKfUkhtC6AZO01tuVUqOBTVrrucDTSqnbgFTgNDA0fd/TSqm3MQQeYLTW+nR+x5QNd3dQisi6FaEUfDHsvRi3b1/O5eBGlH/1ZXj4YQ699T5/t+hKy4jGmS6C5v+uBKDe/Xfn69zXdOEKpYb/aofQAuD4ceMGzmSXlhFkdOUkNjbDqLbv3DV63nZpOlFMKAm6Xdm3KpUvnKFOKfme5FRn2JHhYg70wbwivS58797XfF7R7LJNrobxjTfC3Llw8CAEBIhm55MCaf6itZ4PzM+ybJTd36/ixJOhtZ4ETCqIcThFKfDygitXrutpCgub98GyLY6Ij3ahXn4Z+vcnadRb1Bv9P3pXrMbtw8cz7ukeVy+CuXONzlrp3RCzInekQm5Y4hIZMu8g/1SsRvVLZ9nRvT8t7Tewear37wdg6vqDjJqzDavWmJTKlkgj37OipdjrdsWKcOHCdT1FYeLIa5xrE5hJk4zwqvDsgR+i2UJuuNRkqHdveOEFWLCAqeG9RLPzSdnpqFiuHCQlFfUoCgxzoA/mneshLQ26dAEfHyb9sIg1k2Yx6be3ePOvL4jq09q4CM6dg3/+MS4cB0jrWsEVbNPXU8N7US4tBX3eLbNRXauWcZ3FxmKJS2TUnG0ZJR+11riZFAotsZ2Ca5QyoxrsvMZxcRDZiyvhXbhn3xkanI5nWqveRMWGXNXezZthxQr46KPMM0KIZguu4VLIUePG0KABZ2bMYVRcPVLTrFRMvsxFL2/R7Gug7BjVXl6lyqgGYOVKI7Slg5HBW6VyBaIatOKTzvfyyorJLN25CW4KMZIQUlMzisBnRVrXCq5gm74e12kQHu4mpmQVWaWMEJCYGKJiE0izaloe2U3dcydZ3Kwzo/uFkXgpWTxrgmuUQqM6g8mTYf16Oq5fT0cg2eTOfdHzmVV+NJagEcb18fnnUKECPPxwtt1FswVXyLUqCBi63asXFSb9QFvfTjy3agphx/fR9fGJPD3kBtHsPCJGdUlmxQqIiIAKFTLqmqZaNT9E3MZj62dwZfw3WPr2xLxuneHpaNvW4WFcuvCEMo9LSU/h4bB8OZENqlFJpTJ+zvuUT0mi06uPM7hd3sqqCWWcihXh4kWwWrN5aks0WsP06Zxv15GHmt3JRe3GicrVGffH+/T6dgy3W+vw8YMdaTl1KjzyCPhkv85EswVXcDlRtXdvPMaPZ+qvr3HJw4vyqUl8UO8KN4pm5xkxqksYtji69rXL03rjRnj+eeCq5wIgyd2TWaE3MWTzfH75d69hVIeFQaVKDo8pGeKCq+Sa9NShA0ydilmdZz6bqXvuJACDQ7PXTxeEHKlY0Xi+dOnq3yWQbLHP27bBzp1YnnkLS/kQrNoo/P1cn+dZOOlJPvzjQ1LPdIXkZHjqKYfHFM0WXMWlRNUePeCdd4itUot/aofy4F0dufH8wcIZYCmjbBnVJTxR0T6OrlP8Nn5KSYEbbgCuei6SU6xYgenht/CQZS63rPsL1q+He+7J8diSIS4UCO3bG8+LFuH/9adQpQqcPWvEkIaGFu3YhJKFzZC+cKHEGtUOY59/+w1MJnzuG4TnH/tISbXiZlKcqlaTV3o9zfg/3oOpu40EssaNnR5bNFsoMDw84LXXCAKCAJo1g02bjFkVyFamV3BO2TKqS7in2j6OLvhYejvo1q2BzJ4LH29PEi815uzem/D/7H3D42EzdgQhn+RYdaBFC/D2htdeM4zpzz83unXGxcGpU1C9unTuElzD3qguoTiMfZ43Dzp1oqW5EVN8r3a2NbYP4XAQ1P30faeJ5YKQV/JcKSYiAubPN2bCly41kmZLUwjWdUSM6hKEfRxdk1NxpPpUw71mzYz12TwXAROgeXPjbzGqhQIg16oD7u5G7P4//xjfuTvvNIzqAweMrp1NmsDy5UU1fKEkUQqM6qyxz50qpRkGypgxQHbNNgf6QNd34aWnjWo6gpBPrqlSjNlsJNN++aVRYWzRIujVq1DGW9IpO7cepaCkns0b/XyPxvRWCbi3bJHjtIzFvRp/3P8i+5uZsXj6FuJIhdKKI89bNtKr0fB0umHg6QlRUXDsGKxda8TICkJulAKj2l6zpwyLpOWu9H45t9zicHtLXCL/m72V19adxBKXWIgjFUorLml2Vsxm41kpY3Zx/PjrO8hSRNnyVJ8/X9SjyDfmQB/M9arA3l0wdKjT7SxxiQyasI7kqu3g1nZ4TlzPtEeklqmQP1yqOvDQQ5CSAnfcYUwZBgbCn38a65KTDcO6e/fCHbhQ8igFRjVk8Ua/vgj8/KBVq2zbZWh2mhHH+rslXjRbyDfXVCmmZUsoXx6GDDEcI2PGGLON9etf7+GWeMqOp7oUhH9kEBdn/NDYQjscEBWbQEq6OEMe7lAFIQeyet4c/uA3bAgffmgkv4BhVJ85Y/zt7m7E6AlCbpQSozoDrWHxYqNfgIP4VNFs4XrgkmZnxdsboqPhiy8MJ4nWMHv29R9sKaBseapLaPWPrEkGMcuiCAZ2+QbSxMk+kUHV8XBTGV4PqWUqFBQ5VR1wmBBj824EBxteDzGqBVcowUZ11uvAEpfInqVRDDpxwuksjWi2cL3Is7QVqTcAACAASURBVGaDkf8C0KCBUQ1k/nx47rlCGG3JpmwZ1cXcU+3oy501yWBU31CO/7KI54AhUReYYE50eLGYA32YNrw9M6PjUcCA1v4yjShcV5wmxAQGGhu0bm0I9TvvwLlzULly0Q5YKN7YjOqLF4t2HLngyIDOqtmj521n4IZ5AGwNDsfRHKNotlDYuJzE2Lu3UcmpBJe3LCzEqC4mOPtyZ00ySJz0E/dt+INYnzqccSuXY3taqWMqFCb239WkFCszo+ON75/NU92qldFx0WoFiwW6di3S8QrFnAoVjOdi7Kl2pNtZNXvBtqMkp1ppc2gbRytVZ2VKRYdGNYhmC4WL/Xc1OcXKZ0v28Gz3Rtm/g717w8cfw5IlcPvtRTPYEkLZiaku5tU/HH25LXGJGUkGbgp8Uy7y+MQ3OVqlBk/2f1WmB4ViRWRQddxNRjUaDcywxBsVDGx1qTt1MuqfgtFYQBByolw5I/a4GBvVjm4k7TXbw91Er7DaeLop2h7ahiUgjMiGUolJKB7YvqsmwAqsiTnFkIlR2SvPdOpkdGSeP78ohlmiKBCjWinVUym1WykVo5R6xcH655VSO5RS/ymlliqlAu3WpSmlNqc/5hbEeBxSzD3Vzr7cQEaSwU9tK2CyWin33hj63NvT9aQDQSgEzIE+DIyoh63IY1paeqJVeDgcOmQIs6+v4bneuLEoh1rmKRGarZQx1VyMjWpHN5JApsSwwe0C+L1HLWpdOE3zQbeJZgvFBlsSY8cQX0wK52X3PDzgxhth5cqiGWgJIt9GtVLKDRgH9AKaAYOUUs2ybPYvEKG1bgHMAD60W3dZax2e/rgtv+NxSjFPVMzpy20O9GFE12AaJxwEIKRbe0Z0DRZxFoodA1r74+Vx1UuXMZPi7w8Y0+UxgU1JWre+CEdZtikxmg3F3qh2diNp02ybRjf/x4inDuzfs4hGKgiOMQf68Gz3RplmV7LOgFviEllbuwns3g0nTxbRSEsGBeGpbgvEaK1jtdbJwK9AP/sNtNbLtda2jg9RgH8BnDdveHkZsZypqYV+alfJ9cu9bRtUqQJ16xbdIAUhB3Iq32SLP/3dVAuv+INsid7r8BiWuETGLY+R5hfXj5Kh2VDsjWrI4UbSxqJF8H//BwMHQtOmRTNIQcgBV3T708s1AIiZvSjb/qLZVymIRMW6wCG71/FAuxy2fxhYYPe6nFJqE5AKvK+1/sPRTkqp4cBwgICAgLyP0svLeE5KMmrlFlPM/pVZGTeTNa27EtC/d2Zv9LZtEBqaYxdFQShqnCVb2eJPt9QKAeDg4pW0bB2SaZtraqkr5JWSodlQIoxqm0HisCwZwLPPGsb0Dz+IdgvFllx1u2YISW4enF/yDwy/N2O9aHZmCsJT7UgltINlKKXuBSKAj+wWB2itI4DBwGdKqYaO9tVaT9BaR2itI/z8/PI+SnujujgzZQo1fv6e/qv+n707D2+qyv84/v52Yy1Qyr60Zd+3FqGKjICgoijuqLiNCzqD8xtHZ9FRcQZ11BmXmVEcF0TRQURARsQFQXBBLEIqyCZSCoUCAkJBEKRLzu+Pm5Q0Tdq02Zvv63n6JLm5yT1Nk09Ozz3L2xXfmMZYlWrnoC+looxz3MDmNl0ByPwhv9I+tVpSV9VUdGQ2REWlGqjU3aPczp3w7bdwyy2nZjNRKoo4c7ssMZENbbvRLW9dhfs1sysKRJNtIdDR5XYHYI/7TiIyGrgPOMsYU16zNcbscVzmi8gnwCBgWwDKVVH9+tZlJFeqT5yA+++3ri9bZnVVSUiACROsL5ZDh7RSraKWa4veyfkZtN9ZsfuHraCI3YdPkBAfR1lZDZbUVTUVHZkNVqV6//6gPHVIOBc68rLgi1KRzjW32x8eQ+Pp0+Cbb6B/f81sDwJRqV4NdBORTsBu4CqsFoxyIjIIeAE4zxiz32V7CnDcGHNSRFoAw6g4ICZwIryl2lZQRNHz0xm9axfceiu89JI17diQIbBoERx3dG/s0ye8BVXKD+WnGDMHWsHs4HoKcdR2Gx3Gn8uFZ3iYL1UFQnRkNliV6vzKZzQigdeV6FwtXQqtW2tuq6hWntsZv4V358FZZ7HpjYVMXHmM4lI7CQJXndaRS7M6xnxm+939wxhTCtwBLAY2A28ZYzaKyFQRcY4M/wfQGJjrNg1TL2CNiKwDlmP1z9vkb5k8claqI3AGEGeFYteSzzmeWI91k+627liyxPpCOX6c4tZtKYtPYF2zjlU/mVLRoH9/2Lq1/J9F5ynEgbs2M33Og4z+fGHMh3OwRE1mQ8R2/3Bm9pMfbfE8r+++ffDuu5R8tIQt/YZi23k4PAVVKpA6dYIVKyA+nvqPTKW41E7jE8eYM/NuLn7nJc1sArSiojHmfeB9t21TXK57PPdljFkJXheXCqwIbql2Vih6HtjBlpYZrDwaz4DMTKtS7WjhuG7sHziY2IDCBXnMSm2hb14V3QYMALudzR/nsKxxGikNk0hKiOPqb6yR5X12rA9zAeu2qMhsiNhKtXs/0n0zZ0P35nDVVdYOv/sdzJ5NIvBSvS4smp4T8wO4VB2RkcHeq28kY9pTdO13Jfd99AKD9myhbMEeeO6xmB87ELnTYARaBFeqszunkhQv9DhQwNKeZ1h9ks49F/7+dxg4EHtcHOtT0zieUJ94l7mrlYoW7qfKN6Sm0xd4/YWFvNlvDEkJcUwdmcbFT3wBQMrXq63BuTpbQmxzVqoj7L3gHLxVUmqnqf0k5z5xDxw9CiUlcOWVsGgR+cNG83SLTD7odgZGc1tFGffMdt5OaZjE83EDWIaw4OX/o1HJz/ww4VpazPkvzJsHN9wQ7qKHlVaqQ8hbH7ys9BTeuiiD5o/8SPbFI0lLT4FLLoFHH4Xp0ynO6Iy9QUPiS3UggIo+7lMuTRnXh4eWfI8tsR5jNq+g6/fb+dfwibR8bwGJP5+wQnnmTCgosFZfVLGrcWNrwPbJk6cGm4dQVZntHLw1buVC4o8ehd694aabYPduOHqUsptvYUl+E4zmtooynjJ76qKNFJfaiRPB3rA58/uezaj81Xz6wFOcf//t8PVX1lgwrVTHiDBXqj3N5QicCuxD1mqJaWc5posdPNhaha6wkPqZA6ueB1WpCOZ+qvyDDXs5WQZbWmQwMt/GSGwcSGnFkAO5VnenO++0KtUrV2qlOtY1aWJd/vhjyCvV1WZ2egpZac3gNzPA2V2vd2+4915o1IhuV49n1r4Tmtsq6njKbOdtjCEuTrh/7B0kJsTx+vXDrLNIv/qV1e3ppZesyRZiVOxUqsM8pZ77m3R+biFv5xaWB/bS0q+sJcv6Wd0VbTsPkzh4JP0LX4f+/b1OzK5UpHM9VZ6YEMfYvm1ZveMQM0+7iG/2bGHUkXzuyl1A4qGD8PTT1rSRjRtbleprrqn+AKruatrUujxyBFq1Cumhq8vsWbdkk1VUABs3wksvYTsqHLjtPs6b+n9w3nlQvz5Z6fU1t1XU8ZbZzttTxvWh6HhxxX8Wf/Mb+PBDmDzZqsdkZ4f3lwiT2KlUh3n2D/c36Q9HT3KyxI7BCuyf1uRaLdPNm5e3kAxo2Ic5wNa0nnSr7gBKRShPK871aJNMTn43+nZOpeOK9+HaayEpybpMSIBhw2DuXPjzn6Fdu3D/CipcXCvVIVZdZufkHyRr/WcAfNN7qNWqXdKJX577Ky69dSI6iZ6KVt4zu4qzLvHxMHu2ddbm2mth7VqrcSTGxF6lOkwt1c436fzcQn44epJlW/aXL2EWHx9H2o5vrRkRONVCsqpjX8be/CzjOg7USrWKau5nWircbn0Z3H03nH02tGhhbXvySWuO9iuvhOXLITExDKVWYeesVB8O/ZR0rhWLoydKeOnz/AqZnd05FZ77HNLT+fxkA6tVG+HVQRfQPKm1VqpVVKsys71JSYHXXoOzzoL/+z94+eWIGmAcCoFYpjw6RMBARYC3cwtZsmkfpWWG7J3f8LvPZ3FtjyY0yPsOTj8dONVCEi+wvW1nsru0CGuZlQqq+vXh66/hhRdObevTh/xH/wVffEHhg38LX9lUeDVrZl2GoaUarIpEdudUpq/YTpmjRi3A5VkdrP7Un38Ow4dXyGwdlKhimS2tL6sn/gpeeQX++tdwFyfktKU6hJwt0AZIKCvlsQ+eIePwXnaOzbR2cFSqXVtIUhomkZN/sHy7UnWRrbg+Oau/rzB908QD7Xmm61CGPfEo6y+4hH7D+oe7mCrUwtj9wykn/yBldlN+Oz5OuCyzA+TlWYu8DB+uma1ijqeZccoH97Yfyz8GbOWyv/4VsrLgwgvDXNrQ0Up1CLn20ZuwaRkZh/cCkPbsExAXZ53udnC+Sd1Hn2tIq7rG0ywLzn9AHxxzG0um/5p2110BK5Zr/+pYEwGV6uzOqdRLjKO4xE5cnDB1fF8rh2cssHYYPhzQzFaxw1NmZ6WnnBrci3DfOb9mxJEdpE6eDCNHxkz/6tjp/uGc/SOMy5Q7WzPuPrsrU9YtgKFDrVGy+/ZZyza7vencR587Wz+UqgtsBUVMW55XPqOC6/s8pWEScSJ837QVv7piCo32FnLgtDPI3bI33MVWoZScbF2GsVJdntvn9mDObadzzdA0647Fi6F1a+jZs3xfzWxVl1WV2baCInYfPkFCfBxxQEl8Ii9ddw+msBB+/Wuw28Nd/JDQluoQy0pPIWvjl7B7F/zraVi3DtavhzPOqLSv++hz7aen6grXlo6EOCEhPo6yMut9ntIwiamLNlJmN8QJfJHen9su/BMz5z7I9DumYKb/XVv/YkV8vFWxDsNARVeVBmn9/DO89541y4HLQCzNbFVXVZfZzvviBBAoM/B8cWsaDJ/Ib19/3foH9B//CPevEXSxU6mOj7d+ImGZ8unTrTlXL7wQunWDhx+2To+48TStjVJ1gWuLXpndMGFIR9o3a0B259QKYw/sxlqh+tPOWXyWMYhfrXiTeWtvIyt9cLh/BRUqzZqFtaW6kj17YM0a+OknuPTSCndpZqu6ypfMdua1cXncP7MnMLpJCX2eeAImTbLqPHVY7FSqwWqtDnelet8+ePdda9W4pCSr28fWrdC5s8fdddEXVRe5t+hdltmhwvvceV98nGAHSssMj478JQtn/o5r7rkBMj+Ejh3D9wuo0GnaNHIq1XPnWtM8tm5tVfZHjKi0i2a2qotqk9kAiYnxlE55ED6cD88/b02XWocFpFItIucB/wLigenGmMfc7q8HvAZkAQeBCcaYHY777gVuBsqA/zPGLA5EmTyKhEr1u+9CaSnccMOpbV26hK88SoVBVS167vcBzM8tREjj84EzOePe29l2/mUcXrRYKy9+iJrcjqRK9bPPWkun799vZXhSUrhLpFRI1C6zoU+7pswpPELckFH0enkGCQ8/DA0ahOm3CD6/K9UiEg9MA8YAhcBqEVlojNnkstvNQJExpquIXAU8DkwQkd7AVUAfoB2wVES6G2PK/C2XR5FQqf7yS2jeHPqcWhrA09Q0StV1VbXoeVp4wFZQxNVrdjF54IXc8eUczvjnYp6789wK0znp58g3UZXbTZvC998H5alr5Ntv4bPP4NFH2TBkFCuPJ5JVUKTvNRUzapXZL35JcZlhe9oIZq/8iPyX36DzHTcDdTOzAzH7xxAgzxiTb4wpBt4ExrvtMx6Y6bg+DzhbRMSx/U1jzEljzHYgz/F8wVG/flhn/wAgJweys8sHtzg7/z/50RYmTs/BVlAU3vIpFaFy8g9SUmb4qNtQ4o2dX2zJKZ9dQT9HNRY9uR0pLdXTp0NCAuvOvpjLlx/ksS/26HtNqSo4Mxvgq459+CmxPsc+/hSou5kdiEp1e2CXy+1CxzaP+xhjSoEjQKqPjw2ccLdUHz4MmzaVL/ICOgWTUr7K7pxKYrywsXUXdie35Nxtq8pPNernqMaiJ7cjoVJtt8Obb8LYsaw4lqDvNaV84MxsgLK4eNa3606XbeuBupvZgehT7Wlhd+PjPr481noCkUnAJIC0tLSalO+UcFeqv/rKuszOLt+kUzAp5Zus9BRmTzqd+bmF7Fw/mlHL/0dcS2uqTP0c1VjQczsgmQ2nZv8wpsL0dSG1ahXs3g2PPabvNaV85JrZAqQfPZtGLz4Dx4/X2c9RICrVhYDrMPwOwB4v+xSKSALQFDjk42MBMMa8CLwIMHjwYI8V72qFu1Kdk2N9KZx2WvkmnYJJKd+V99uTK+GD2bBxI5x2mn6Oai7ouR2QzAarpbqkxOq6F64BTvPmQWIiXHghWU2b6ntNKR9V6GudMBKeexpsNrKGD6+Tn6NAdP9YDXQTkU4ikoQ1gGWh2z4LAed0F5cDy4wxxrH9KhGpJyKdgG7AVwEok2fhrlSvWQO9ep1aetchKz2FySO71pk3lVJB16uXdbl5c/mmrLaN9HPku+jJbWdehmsBGGOsSvU555SXRTNbqVpwnqX/8ksoKyNr+lNMTthbpz5HfleqHX3t7gAWA5uBt4wxG0Vkqohc5NjtZSBVRPKAu4B7HI/dCLwFbAI+BCYHbQQ5WJXqcA5U3L0b0tPDd3yl6ghbfApl8Qnszfna2rBpkzXV2eLgzexWl0RVbjsr1eHqV71xI+zcCZdcEp7jK1VH2I4ncLh9Oj/NehPuucda+O7SSyNjdp8ACcg81caY94H33bZNcbn+M3CFl8c+AjwSiHJUq359OHQo6IfxOk3MgQPQr1/Qj69UXWYrKGLiTBvvNmtLwcc57CkoImvBAuss1EMPwbnnhruIUSFqcjtElWqvuf2pNVsBo0YF9fhK1WXO2T4uGHgpj37wDHzzNYwdC8uXw223wTvvhLuIARFbKyo2bmwtLRtEzjdOcamdpIQ4Zt2SbQW0MValumXLoB5fqbrOOWo8L7Uj3X/Yycu5hbSb/TZt4uORL76AL76AYcPCXUwVKM2aWZdBrFR7zW2w5qbu0AEyMoJ2fKXqOmduz+97Nt+1SOfWA1/T8YmHGfTGC1aL9Q8/QIsW4S6m3wLRpzp6NG4Mx44F9RBep4k5dszqetKqVVCPr1Rd5xw1vj21A+lFe/jk029otWktr5w2ntKU5jBtWriLqAIpBH2qvea2MVal+qyzwjfziFJ1gDO344D1bbpyZ/8ruPqN9Xw76Exrh+XLw1q+QNFKdYA53zjxQsVpYg4csC61pVopvzhn+ug2YggJxs5VqxcRb+y81+0MCvpkgc0W7iKqQApB9w+vub11q9Xf8xe/CNqxlYoFztwe1q0FcUL5P7DLmqRDcjIsXRruIgZE7HX/OHYsqPOdep3aa/9+61JbqpXyW1Z6Clw2Ah6FW79awIFGzdjcsQfHk3ZhX/kxa7fsIbNHu3AXUwWC85Twvn1BO4TX3P7sM+vyrLOCdmylYkVWegp3ju7O6h2HyuenbpbckO19T6Pt4iXUD3cBAyC2WqqTk62VsU6cCMrT2wqKmLY8D6DydEvaUq1UYPXoASIkNqzPZ48+z/0X9Wd6UUPi7HYe/sf8OrPsbcxr0ABat4aCgoA/tTOzbQVFnqfJW7/eaozp3j3gx1YqFjn/gb3rnB5MGdeHqYs28lqjrtQv2M76FevCXTy/xV5LNVit1Q0bBvSpqxzoAtpSrVSgNW4MCxeS0L07l3XvzrTleWxMtVbu6/z9dnLyD9ap+U9jWno67NgR0KesNrPB6v7RrZv2p1YqgJwLwkxbnkdxqZ3P0gcBcGjuAjhzQJhL55/Yaql2rVQHWLXr2GtLtVKBN25ceStidudU9rbswMn4RHod3Flnlr1VWDNvBLhSXW1mA+TlQdeuAT2uUsriHMuwo2VHvmuZTmbOR+Eukt+0Ul0LrqcMnVwHutSLM4zZsx7KXNZD2L8fGjUKeAu5UsqSlZ7Ca5OG8WOnrlxe77C2UtclGRnWAix2e60eXl1mVxic6FRaCtu3a6VaqSBx7QrS6LprSP7qSygsDHex/BJb3T+Sk61LPyrVnk4ZgtXqMWVcH4qOF3Nu3iq6Xnst5NwB//63depQ56hWKuiy0lMgO6vOTM+kHDIyoLjYmomjXc0GoFbVzePSzA6I47LSP2E7d1oVa61UKxU0zq4gtL8RnnoU5s6F3/0u3MWqtdiqVDtbqo8erfVTuJ8ynJ9byNu5hRUCu+vSTdbOzz7L5zSj4e/vIuvAAe1PrVQo9OsH//2v/iNblzgXXtmxo8aVam/dPFwr2pdmdgDcVlXMswad061bgH4JpZRX3bvDoEEwZ45WqqNGALp/OE8ZOqeDEaC41E5i8UlmvX4/u5P/QFZuLid69GJVWWOyXnyS8xJ6saRwLw0zOgbm91BKeTdihHW5dClcfXVYi6ICJD3dutyxA844o0YPdc/s7M6pPlW0P6q3kTTQlmqlQmXCBLjnHqvbVadO4S5NrWif6hpy7QM065ZsLs3sQFJCHMN2rSdr92aGf7IAbDZ2ZPTioVG3UL+0mDs+eZ2y/fu1pVqpUMjKgtRU+OCDcJdEBYprpbqG3DM7Kz2lcn/qTs0peehhnpv9II+8/2+aHDnEobUbrTEwbdoE9ndRSnl25ZXW5VtvhbccftCW6loo7wPkMOuWbBJ/+wYAKR8vhuJiGp0xlN0lHXk9cxw3rlmIxImeilYqwCqcrnd+JuPj4dxzYfFia2BbXGy1HdRJjRpZ+VnLuardM7vSYi9HdsHLT7EjpR3DdnzNeVtzaNC6hdVKrdPpKRUwHjPbqVMnGDoU3nwT/vjHqPzs+fVtIyLNRWSJiGx1XFYabi8iA0XkSxHZKCLfiMgEl/teFZHtIrLW8TPQn/JUyzlQ0Y8+1Z5kpTWj/7oVVutYcTEAaaPPZNYt2RTc/Wf2p3VB7HatVCsVQM4BaE9+tIWJ03MqLvZy3nnWjDtffx2+AkaoqMttpwBPq1dhsZdFiwA4sngp/3lqLkc7pFN/R772p1YqgKrMbKdrr4W1a+Gyy2DNmlrP+BMu/jbh3AN8bIzpBnzsuO3uOHC9MaYPcB7wTxFp5nL/H4wxAx0/a/0sT9Xq17darQI0T7VzmqYNy76ywv6++04dY4A1gfmcjQeZeN4fyG/enpd+TtVV3pQKkCrnGT7nHKuVY9Gi8s/pG6t2VppWLUZFV247BWABmEpT6+3caQ1oXbQITjuN0lZteP5APUZd8jB/vPAu/n3m1fp+USpAfJob/le/gscfx/7+B3DaaeT3H4ptx6HQF7aW/O3+MR4Y4bg+E/gE+JPrDsaY71yu7xGR/UBL4LCfx645EasLSAAq1a7TNP1oe4e+YP1ntXKl1cm+USNyvtpLcamdbSntGXXrC8gxqDc9x/PKXUqpGvE0AK1c69YwfDgnXp/FxJLTOFlqqF/8MyY+nmfq14v1z2B05bZTRga8+26tu/RUmlrvpiFkjRgOJ09aZzX+8pdTX/oSz1u9RyHfw3Oa2UoFRJWZ7RQfj23CrUze04HrVy3g1znzmPCXV/jjX2+Kis+gvy3VrY0xewEcl1WOxBORIUASsM1l8yOO04tPi0g9P8tTvQBVql3/4xqSv5bDHTIgLQ1eeQWWLAFOvYGcvYIMVfx3ppSqEU8D0OBUa2TBOeNpsG0rXfZs46KNy1k17XqmLHleP4PRmNtgVaqdFeBacG8l2/nOh1ZL9aFDYAxceKFmtlJB5C2zoeJZpJz8g+yr35RnT5/AsaQGXG77MGo+g9W2VIvIUsDT8Of7anIgEWkLvA7cYIxxdpK5F/geK7BfxGotmerl8ZOASQBpaWk1OXRFyckB6VM9et9mzp7xW+4adxdDCjfy86VXWHc4B0Ny6g00P7eQebZCysqq+O9MKVVj7gPQXFsjX/u5LSvj43lp/kO0+/EAJ+MTGbtlJQ9fcEed/wxGQm4HLLOdXOeqrsWMHO6tZGeuXmrN7rFiBXz1FQwcSJaIZrZSQeSe2XAqt0+W2ImPE245sxOJ8cLxpAYs7PULLtn4Cd+2SAxTiWum2kq1MWa0t/tEZJ+ItDXG7HWEr8cmBBFpArwH3G+MyXF57r2OqydF5BXg91WU40WsAGfw4MGmunJ7FYiW6o0b6XH7dXDkCDM/e47kk8dJvug8oPLIVufPZZkdvI94VUoFhGtr5L56TfgkI5Oz8m0svWoyDXt244y/3Mnbg5PoWcc/g5GQ2wHLbCfXafWys2v88AozfnRsQsvs6+Cii7A1zyCnezLZOw9rZisVBjn5BzlZYscApXbD9BXbmTq+Lxv2HOFI8+tp8KfFDMr5CPrdGu6iVsvfPtULgRuAxxyX77jvICJJwALgNWPMXLf7nMEuwMXABj/LU71AVKofe8zqn33xxbT83/+sbSNGVLkcrqf/zpRSgeVsjXQG9N0X/I7mJ49x2YSRTB7QHKbeRc+1X8Al54S7qOEUfbkNfs1V7VSewx98AAcPknf2hZrZSoVZdudU4uOEUrv1v7fdGIqOF/O3S/qB6QszpsLrr8OtkV+p9rdP9WPAGBHZCoxx3EZEBovIdMc+VwK/AG70MAXTLBFZD6wHWgAP+1me6tWiUl1pxHhenrXAxEMPWbd79YI2bXwb2aqUChpna+TVQ9NISojjaMMm7GnZwTp937y51cKpi8JEX26D1XUvNdWnuaorZba72bOhWTOWdBygma1UmGWlpzB1fF8S4oQ4gSTXLlcicN118PnnAZ1SM1j8aqk2xhwEzvawfQ1wi+P6f4H/enn8KH+OXyvJybB1q8+7e2x93r4dxo2Dvn3httusS3wc2aqUCirX0/fzcwupsHzA2LHwwAPWYLdWrapeiKCOisrcdvJhruqqzhgCcOIELFgAEyYwpGc7klbs1MxWKsyuGZpGjzbJlTMbYOJEuP9+eO01mDIlonM7tlZUBKulugYDFd1bnxd+uZWsffvYndKG9oDt3ketP25BUeVVuiLsj61UrHk7t5DiUjvzcwutypWzUr14MbZfjKu68qUiT3o6bNpU5S7uS8nJTwAAIABJREFUmT0/t9DK5IwUsp6YQtG2naQcO8Z3Iy/QzFYqwlTK7PQU65/p88+Hf/yD9aPGM3HxnojN7disVNeg+4dr63N8nPDVcmuFtqfzislctZOpizZW+uNG0h9YqVjlqTtW1lmDoFUr+OADcjqcXvl+/exGtowMq/uOMV6XMHbP7Hm2QkrL7Jyfl0PWvOdoirCnSUsu3pTI647GEP27KxV+HjPb+dl87jno25cOV1/CG/YkHhvxS2xpfSMut/3tUx19nJVq49tgdNd5Fa8Y3JF2h6yB7zuSW/HBhr3aH0+pCOWsXMUL5af2bbuOsHnQMEo/+JDs9GYkxQs9fyjQU//RIiPD6r5x4IDXXdwzu7TMjt1umLRiDvtadiDrt28w9sZ/c9IumtlKRRBPmQ2OMRL5JWx/6AkaUkbPAzu40fZuROZ2bLZUG2MFc8OGPj3E2ZJhKyji/Res2ae+T23Lr/u2ZfWOQ9ofT6kI5H5qH2Di9BzG1O/GM4cX0GjtGpZgo+PLU9j87jJ6RVBrh/KiUyfrcts264yDF66ZPT+3kMF5X9P/+62suucxTiQ01cxWKgJ56o7lOkbimYQ0Zq1YS8cH/sCYt2Yx+5p+DIqw3I69SnVysnV59KjPlWqnrPQUOnQUSurV51+/HUtWRnN6tEnW/nhKRSjXU/vTludRXGrn04xMjiY1oO1vbqNpkdXi2eu7r4GRYSyp8knv3tblhg1w+unV7u78krbfvwgTH8/QB37DrAMnNbOVilDu3bE8dgm56Vp4/WUGbfgSel0RxtJWFpvdP6DWc1W3PriXxM6dyMpoDlhvgMkju2o4KxXhnKcWf2rQmElXT6Xh4UOcjIunpEUr+PLLcBdP+SIjw8rw9et9fkhWegqnHd6J9OwJDRtqZisVRVy7hMTHCXsOn8CW1hdat4a5c6t/ghCLvZbqJk2sy0OHoEsXj7tUOV3L9u2nTkEqpaKG66nFlIb9uKBRCvE//8yvV83j3M+/IKmKwW8qQsTFWVOYfvNNhc3VTrG1di2cdVaICqmUChRnbs/PLWSerZDZX+1kfm4hK0aMocVH74HdbuVChIickoRK//7W5Zo1Hu929t958qMtTJyeU3nxAK1UKxW1nK2UG/ccYWtyGza3zMDWrgdJ+/bCrl3hLp7yRb9+Vku1Y7B5tZn9ww9QWAgDB3p4MqVUpMtKT0Gwun84u4Gs7dQfioqqnWIz1GKvUt2pE7RtCytWeLy7ylUR9+yBI0ege/cQFVYpFWi2giLmrtmFc/6fdR0d/XS1C0h06N/fOtO4Zw9QTWYDrFtnXWqlWqmo5J7Z8fFxtD5/tHXj88/DVi5PYq9SLQJnnum1Uu1tShcAVq2yLocMCUFBlVLBkJN/kFK7Fc8C9DnvTGvQck5OeAumfNOvn3Xp6FddZWbDqUr1gAEhLKRSKlDcM/vyrA70O3MgtGvntS4XLrHXpxqsSvXcubBzJ6SlVbiryhW2vvoKEhK0xUOpKOa6OEhiQhyXDOlkVdSclS8V2Vwr1eedV/2qiGvXQvv20LJl6MuqlPKbe2ZfltnBaiAdPjziWqpjt1IN8MUXlSrVUHlKF+bMgQYNrJbqAQOgfv0QFVQpFWgeK2H9+sH//lflSn0qQjRvblWSXQYrel0V0RjrSzcrK4QFVEoFktd/nM8806qfFRRAenp4C+kQm5Xq/v2taZm+/BKuvtrjLhVGk0+ZAt9/b40yvf76EBdWKRVolSphffvC9Omwbx+0aRO+ginfOAcruvA4A4jNBjt2wAMPhL6MSqmA8fiP8/Dh1uWKFVqpDquEBOjTBzZu9Hi36wo+SfHCpp27iPv5hHWn9qdWqu5x7VKglerI178/LFsGJSWQmFgxsxPimHVLtvUFPHeulfcXXxzuEiulAq1vX2ua5M8/h4kTw10awM+BiiLSXESWiMhWx6XH2fRFpExE1jp+Frps7yQiqxyPnyMiSf6Up0Z69/Y6FYvraPLGRw9bFWrnSoxDh4asiEqp0FjXrCMAuz5bFeaSBF9U57ZTv35QXAzffQdUngHk6NP/tmZpevVVGD3a6jKilKozbAVFTPtsO0cyh0TUYEV/Z/+4B/jYGNMN+Nhx25MTxpiBjp+LXLY/DjzteHwRcLOf5fFd795Wl45Dhyrdld05ldP3bOKXtnfpeNy6f8cDj7D4gX9ia9A6ZEVUSgWfraCICQvyONCoGasXflp5nuO6J3pz28nDDCBtTv7IxZs/JTFeyFq1FPLyYP9+lg4cFQt/U6Vihuvc9DNob/U6OHiw+geGgL+V6vHATMf1mYDP59hERIBRwLzaPN5vvR1z027eXOmurNREXln8NPcvf5knMq1lzX//rZ1flXT1vLiAUipqOVs5v22RQbf9OyrPc1z3RG9uO/XsaXXrcFSqs/Zt5ZM37uafC//Bwp4/k7z+a/ZfdzMX3TqNSaaX5rZSdYjrmamc9r2sjStXhrdQDv5WqlsbY/YCOC5bedmvvoisEZEcEXEGcCpw2BhT6rhdCLT3szw+W9+kHQAFn6+ufOcjj5C0by/xZaV0WWstCLGzUar3xQWUUlHLOV3Td63S6fbDTrLTmoa7SMEWlbltKyhi2vI8q3Jcrx706GHNAGIM3HgjSfUSISmJ7jP/Az/9xNqOvdiQmo4d0dxWqg5xnZt+c8ee2JOS4JNPwl0swIeBiiKyFPA0cue+GhwnzRizR0Q6A8tEZD3wo4f9jIdtznJMAiYBpHmYBq8mbAVFXPvhbnIT6rH8f5/S7+obTo0qLSuDf/0LMjMhNxfefx97Uj2ONkkhvszL4gJKqajlnK7pUOlG6q9+h6yf9wPRPadxJOR2oDO70kDEfv2s1qm1a60zjs8/D/Pnw5IlALQ9dyRJH31fPret5rZSdYP7FHtx34yGN9+Ev/8d4uPDWrZqK9XGmNHe7hORfSLS1hizV0TaAvu9PMcex2W+iHwCDALmA81EJMHR6tEB2FNFOV4EXgQYPHiw18q3L3LyD3KyDPJSO9B5/05y8g+eqlTv3g0nTsAvf2m1ghQWEtelC/+99QzviwsopaJaVnoKXDsWHvuDNQ1bnz7hLpJfIiG3A53Z7kuRZ40YYX2R3nST1RXk8sut7F6yBFq1ot+ZA5mVdlhzW6k6qMIUezfdZH3+Fy+G888Pa7n87f6xELjBcf0G4B33HUQkRUTqOa63AIYBm4wxBlgOXF7V44PBeeogr0WadbrXtQVj2zbrsmdPa/Q4QFoaWekpTB7ZVYNZqbqqZ09rufLc3HCXJNiiLrc9LkV+883WbExr18LYsZCaeuoLNTsbRDS3lYoFF15orZj68svhLonflerHgDEishUY47iNiAwWkemOfXoBa0RkHVYYP2aMcc5l9yfgLhHJw+qrF5JXxHnqoMWIM2h79Aey7IdP3emsVHfpYs2BCNCxYyiKpZQKp/h4GDjQaqmu26Iut52Zfdc5PU7NQZ2QYE2Z17Il3HabtWO3bnDrrdaPUio2JCXBddfBwoWw3+OJt5Dxa/EXY8xB4GwP29cAtziurwT6eXl8PhCW1VSy0lPgzhth2iPw3ntwxx3WHfn5Vlh37GhVqt96y+NS5kqpOigzE155xVo9Nc7fNofIFK257XFFtZ49K36JisCLL4a2YEqp8Lv5ZnjqKXj9dbj77rAVo25+a/iqa1drBPmiRae2bdtmLXeZkHCqpVor1UrFhqws+Omn8kVFlFJKRYHeva1uXzNmWDMChUlsV6oBxo2D5cvh2DFsBUXsW7eZI+0clehhw2DAADjzzPCWUSkVdLaCImbbHbPLLVsW3sIon1WYak8pFTMqffZvvtlaKXtV+FbGjalKtcfwveACKC4m743/MXF6DvUKtvPe8YbWPq1aWYNgevUKX6GVUkHnnLLtvu/srG3fg+KHH4Gffw53sWJedRVm15XVdIEXpWKHx8/+hAnWYPMwDliMmUq11/AdNgwaNOCnxUuof+xHmv18jIImrXWhAKViSPmUbQhPDL+epL174D//CXexYpovFWZPU+0ppeo+j5/95GS48kprqs1jx8JSrpipVHsN36QkOOMMum2y0eXoPgB2p7bThQKUiiGuU7at6TqIH4edBY8/Xqm1WrsahI4vFWaPU+0ppeo8r5/9m2+2KtRz5wKhz2y/Zv+IJs4/gOvqWraCInLyD3Jml4H0X/YU942xKtW/vukceuu8pkrFDPcVuvb2vosm14yn4MnnSL/vLsDLqn6aE0FTVWanNEyi6Hgx2Z1TK/zd9O+hVGxwz2yAacvzyO7Um6wePWDmTGyjLg55ZsdMpdrTH2Di9BxOltj55FhL5hpD3+f+zonuPel9zhlhLq1SKtScU7bZCooYvzGBN9t2p/kT/8B21XVkdWnpeVU/rcQFTVWZbYA4ofyLcvLIruEtrFIq5Fwz27XyvHzMONr+5598vS4/5JkdM90/gAqrazm/IA2wrm0Pfk5IIqmslOXX/bbC2vF6ulep2PJ2biEnSw3/GXo5aYe/5/vZbwPa1SAcvGU24LVbiGa2UrHl7dxCTpacqjyv7D4EysoYtXNtyDM7Zlqq3Tm/IItL7BQnJPJZp0yanfyJ1tdeWb6Pnu5VKrbYCoqYu2YXBljabSj7Gjdn4Idvwf23VWo51SwILdfMtmO1VLt/UWpmKxVbXDMbQOKEr9t2Z3yzFDrnLGdp+y5sTEqhxU3XhSQLYrZS7foFmdIwibzRr5CdnkJmRvPyffR0r1KxJSf/IKV2K57L4uKZ028Mk1fO5ZuV6+l/Rj/Pq/qpkHDPbGefate/h2a2UrHFNbPBWvflDdtuhrYfwIWzZtEB6ACwdA7Mng0ZGUEtT8xWqsHLsrcuPA2UUUrVXc7PvLPf7lsDzuGOL9/i+PQZkPko3HsvuxOT+aTPmfQccZpW2EJMM1sp5cr1My8i2I3BbuC9HsO4cOMn8NhjbE9Ipt2D9xA/cBAJb8+HUaOCVh4xYVzOsbYGDx5s1qxZU6vHOkeP+3r6tqb7K6Wim62giPm5hcyzFVJWZmfBa3fRqUMqyff+ES65BICfE5J4aPQk+jz4e67JTq/R84uIzRgzOBhlj1T+ZDbULIc1s5WKLa6zAk1dtLH8n+o5l3SlNLUFE6fn0ObAbp5872lW/P5hzrx4RI2yoSaZHVMt1VX1t/MWxHq6V6nY4vzMX5bZgZz8gyQUjqLhmy9z8PU5JDdoxKgbnuFvHz7D1MXPMa5Db3q0vUozIoi85bZmtlIKKn7me7RJZn5uIQKUprYo7xK2o1lbLrvmcWSv8J/pOUEbb+FXpVpEmgNzgAxgB3ClMabIbZ+RwNMum3oCVxlj/icirwJnAUcc991ojFnrT5mq4q2/nQ5uUUq5c2bAU2XtmV5aQtMFb/H9mWfzfUprbrjyr2Tu/pYtLdKirt9uXchtQDNbKeXR27mFFJfamZ9byJRxfU516RPBENzxFv5OqXcP8LExphvwseN2BcaY5caYgcaYgcAo4Djwkcsuf3DeH8xgBu9TYulSt0opT3LyD5LTticlcfEkGDvbBp3B1PF9iY+P5+uOvUmKzn67UZ/bmtlKKU/cs6HoeDGzbsnm6qFpIZlez9/uH+OBEY7rM4FPgD9Vsf/lwAfGmON+HrdWvE2JpYNblFKeZHdO5ZmGjVjXrgeDCzeResk4zhqaRo82ydHcb7dO5LZmtlLKnaf6nHuXvmDmtl8DFUXksDGmmcvtImOM15KKyDLgKWPMIsftV4HTgZM4WkyMMSerO66/g1480cEtSilPbAVFHJn2PJm5n9JsyQcg4tfzhXugYjhyWzNbKRUqgc6GmmR2tZVqEVkKtPFw133ATF/DWUTaAt8A7YwxJS7bvgeSgBeBbcaYqV4ePwmYBJCWlpZVUFBQza+mlFKRJxSV6kjIbc1spVRdENDZP4wxo6s40D4RaWuM2esI2v1VPNWVwAJnMDuee6/j6kkReQX4fRXleBErwBk8eHD0zQOolFIhEgm5rZmtlIo1/g5UXAjc4Lh+A/BOFfteDcx23eAIdEREgIuBDX6WRymlVNU0t5VSKgj8rVQ/BowRka3AGMdtRGSwiEx37iQiGUBH4FO3x88SkfXAeqAF8LCf5VFKqYCzFRQxbXketoKi6neOfJrbSqk6LVyZ7dfsH8aYg8DZHravAW5xub0DaO9hv+CtFamUUgFQ1+ax19xWStVl4cxsf1uqlVKqTtM5kZVSKnqEM7O1Uq2UUlXwtmiUUkqpyBPOzPZ38RellKrTvC0+opRSKvKEM7O1Uq2UUtVwrsillFIq8oUrs7X7h1JKKaWUUn7SSrVSSimllFJ+0kq1UkoppZRSftJKtVJKKaWUUn7SSrVSSimllFJ+EmNMuMtQYyJyACioxUNbAD8EuDi1ESnlAC2LN5FSlkgpB2hZvKlpWdKNMS2DVZhIVAcyGyKnLJFSDtCyeBMpZYmUckB0l8XnzI7KSnVticgaY8xgLccpWhbPIqUskVIO0LJ4E0llqWsi6bWNlLJESjlAy+JNpJQlUsoBsVMW7f6hlFJKKaWUn7RSrZRSSimllJ9irVL9YrgL4BAp5QAtizeRUpZIKQdoWbyJpLLUNZH02kZKWSKlHKBl8SZSyhIp5YAYKUtM9alWSimllFIqGGKtpVoppZRSSqmAi4lKtYicJyJbRCRPRO4J8bE7ishyEdksIhtF5LeO7X8Rkd0istbxc36IyrNDRNY7jrnGsa25iCwRka2Oy5Qgl6GHy++9VkR+FJE7Q/WaiMgMEdkvIhtctnl8DcTyb8d75xsRyQxBWf4hIt86jrdARJo5tmeIyAmX1+f5EJTF699ERO51vC5bROTcEJRljks5dojIWsf2oL0uVXx+w/J+iRWa2RXKE/bMdhxTc9t7OTSzNbMtxpg6/QPEA9uAzkASsA7oHcLjtwUyHdeTge+A3sBfgN+H4fXYAbRw2/Z34B7H9XuAx0P89/keSA/VawL8AsgENlT3GgDnAx8AAmQDq0JQlnOABMf1x13KkuG6X4heF49/E8d7eB1QD+jk+IzFB7Msbvc/CUwJ9utSxec3LO+XWPjRzK5UnojKbJe/UUzmtma272Vxuz8mMjsWWqqHAHnGmHxjTDHwJjA+VAc3xuw1xuQ6rh8FNgPtQ3V8H40HZjquzwQuDuGxzwa2GWNqszBErRhjPgMOuW329hqMB14zlhygmYi0DWZZjDEfGWNKHTdzgA6BOl5Ny1KF8cCbxpiTxpjtQB7WZy3oZRERAa4EZgfqeFWUw9vnNyzvlxihmV29cGY2xHBua2bXvCyxlNmxUKluD+xyuV1ImAJSRDKAQcAqx6Y7HKcbZoTi9J2DAT4SEZuITHJsa22M2QvWGxJoFaKyAFxFxQ9aOF4T8P4ahPv9cxPWf9FOnUTkaxH5VESGh6gMnv4m4XxdhgP7jDFbXbYF/XVx+/xG6vulLoiY11Az2yvNbe80syuLmcyOhUq1eNgW8ilPRKQxMB+40xjzI/AfoAswENiLdWokFIYZYzKBscBkEflFiI5biYgkARcBcx2bwvWaVCVs7x8RuQ8oBWY5Nu0F0owxg4C7gDdEpEmQi+HtbxLOz9XVVPxCD/rr4uHz63VXD9t0iqWaiYjXUDPbM83tKg6qme1NzGR2LFSqC4GOLrc7AHtCWQARScT6484yxrwNYIzZZ4wpM8bYgZcI4GmYqhhj9jgu9wMLHMfd5zzd4bjcH4qyYH1J5Bpj9jnKFJbXxMHbaxCW94+I3ACMAyYaR8cvx2m7g47rNqw+cd2DWY4q/ibhel0SgEuBOS5lDOrr4unzS4S9X+qYsL+GmtlV0tz2QDPbs1jL7FioVK8GuolIJ8d/2FcBC0N1cEdfopeBzcaYp1y2u/bZuQTY4P7YIJSlkYgkO69jDa7YgPV63ODY7QbgnWCXxaHCf6/heE1ceHsNFgLXO0YIZwNHnKeQgkVEzgP+BFxkjDnusr2liMQ7rncGugH5QS6Lt7/JQuAqEaknIp0cZfkqmGVxGA18a4wpdClj0F4Xb59fIuj9UgdpZp86ZqRlNmhuV6KZXaXYymwTpFGpkfSDNbrzO6z/hu4L8bHPxDqV8A2w1vFzPvA6sN6xfSHQNgRl6Yw1+ncdsNH5WgCpwMfAVsdl8xCUpSFwEGjqsi0krwnWF8JeoATrv9Sbvb0GWKeGpjneO+uBwSEoSx5WHy/n++V5x76XOf5u64Bc4MIQlMXr3wS4z/G6bAHGBrssju2vAre77Ru016WKz29Y3i+x8qOZXV6WiMlsx3FjPrc1s30vi2N7TGW2rqiolFJKKaWUn2Kh+4dSSimllFJBpZVqpZRSSiml/KSVaqWUUkoppfyklWqllFJKKaX8pJVqpZRSSiml/KSVaqWUUkoppfyklWqllFJKKaX8pJVqFRVEZIeIjA53OZRSSlWmGa2UVqpVHResoHcsafq4iBx0/PzdsTyqt33vE5GdIvKjiLwpIk0CXSallIo20ZDRItJeRN4RkUMiUigitwe6vKpu0Eq1UrUzCbgYGAD0B8YBt3nZ93rgOmAY0A5oADwTgjIqpVSsCmRG/xfYDrQGLgD+JiIjg1NsFc20Uq2iyWkisklEikTkFRGpDyAi40RkrYgcFpGVItLfsf11IA14V0SOicgfHdvnisj3InJERD4TkT61KMsNwJPGmEJjzG7gSeBGL/teCLxsjNlljDkGPA5MEJGGtTiuUkpFqjqX0SLSGBgBPGKMKTHGrAPmATfVokyqjtNKtYomE4FzgS5Ad+B+EckEZmC1QKQCLwALRaSeMeY6YCdwoTGmsTHm747n+QDoBrQCcoFZzgOIyD2O4Pf441KWPsA6l9vrHNs8EceP6+16jjIopVRdURczWly2ud7f14fXQ8UYrVSraPKsoyXhEPAIcDVwK/CCMWaVMabMGDMTOAlke3sSY8wMY8xRY8xJ4C/AABFp6rjvMWNMM28/Lk/TGDjicvsI0NhLn70PgFtEJMNxnD85tmtLtVKqLqlzGW2MOQp8ATwgIvUd/yRchua38kAr1Sqa7HK5XoDV9y0duNutpaKj475KRCReRB4TkW0i8iOww3FXixqW5RjgOtiwCXDMGGM87DsDmA18AmwElju2F9bwmEopFcnqakZPBDph/X7/wWo51/xWlWilWkWTji7X04A9WCH3iFtrRUNjzGzHfu4Beg0wHhgNNAUyHNsFQET+7Ojb5/HH5Xk2Yg2AcRrg2FaJMcZujHnQGJNhjOng2G+340cppeqKOpnRxpgCY8w4Y0xLY8xQrG4sX9XgdVExQivVKppMFpEOItIc+DMwB3gJuF1EhjqmRWokIheISLLjMfuAzi7PkYx16vEg1um7v7kewBjzN0ffPo8/Lru+BtzlmGqpHXA38KqnQotIcxHp4ihfb+ApYKoxxu7vC6KUUhGkTma0iPQSkWQRSRKRa4FzHPsoVYFWqlU0eQP4CMh3/DxsjFmD1WfvWaAIyKPiCO9HsQbLHBaR32MFbQFWC8QmIKeWZXkBeBdYD2wA3nNsA8DRajLccbMF8D7wE1bfvRnGmBdreVyllIpUdTWjz3X8PkXA7cB5xpgDtSyXqsPEc/cipZRSSimllK+0pVoppZRSSik/aaVaKaWUUkopP2mlWimllFJKKT9ppVoppZRSSik/aaVaKaWUUkopPyWEuwC10aJFC5ORkRHuYiilVI3ZbLYfjDEtw12OUNLMVkpFq5pkdkAq1SIyAxgH7DfG9PVw/0TgT46bx4BfGWPWOe7bARwFyoBSY8zg6o6XkZHBmjVrAlF0pZQKKREpiIAyaGYrpZQPapLZger+8SpwXhX3bwfOMsb0Bx4C3Be+GGmMGehLOCullPLbq2hmK6VUQAWkpdoY85mIZFRx/0qXmzlAh0AcVymlVM1pZiulVOCFY6DizVjLgDoZ4CMRsYnIpDCURymllHea2Uop5YOQDlQUkZFYAX2my+Zhxpg9ItIKWCIi3xpjPvPw2EnAJIC0tLSQlFcppWKZZrZSSvkuZC3VItIfmA6MN8YcdG43xuxxXO4HFgBDPD3eGPOiMWawMWZwy5YxNXBeKaVCTjNbKaVqJiSVahFJA94GrjPGfOeyvZGIJDuvA+cAG0JRJqUCxVZQxLTledgKisJdFKUCQjNb1WWa2SpYAjWl3mxgBNBCRAqBB4FEAGPM88AUIBV4TkTg1DRMrYEFjm0JwBvGmA8DUSalQsFWUMTE6TkUl9pJSohj1i3ZZKWnhLtYSlVJM1vFKs1sFUyBmv3j6mruvwW4xcP2fGBAIMqgVDjk5B+kuNSO3UBJqZ2c/IMa0CriaWarWKWZrYJJlylXyg/ZnVNJSogjXiAxIY7szqnhLpJSSikvNLNVMEXlMuVKRYqs9BRm3ZJNTv5BsjunaouHUkpFMM1sFUxaqVbKT1npKRrMSikVJTSzVbBo9w8V8YI1UltHgCulVOBpZqtYpS3VKqIFa6S2jgBXSqnA08xWsUxbqlVE8zRSOxDezi3kZMmp552fW6gtIEop5SfNbBXLtKVaRTTnSO2SUnu1I7VtBUU+DT6xFRQxd80ujOO2xAnzbIWUlmkLiFJK+UMzW8UyrVSriObrSO2anBrMyT9Iqd2KZwH6tG3C+t1HKrWs6OhwpZSqGc1sFcu0Uq3CrrrWiupGatsKivjn0u98ntDfvSVlwmlpbNm3sfx2SsMk7bunlFJeaGYr5ZlWqlVY+Tv4xPn4kyV2DBDnw4T+nlpSerRJLr8drBW3fD3VqZRSkUozWynvtFKtwsrfMHQ+3mCNuh3WtQV3ju7u8TlsBUXMzy1EgEszOzB5ZNfy+9xbVtz7BPobrjpyXSlVF4Q6s3PyD5LSMImi48UV8re6zHZ9fG1yWzNb1YZWqlVY1WRQiy+Pv3N0dwCmLc+rEKS2giKufvFLisusfnlzbYXMvtVzSLq3igB+h2uwWlIWyrpWAAAgAElEQVSUUiqUgpHZWekplSrAnlq0veWvp5ZsfyvFmtmqNrRSrcLK3yVjfa0A5+QfpKTMlD+uqpB0D/dpy/N8DldvLSM1/SLS045KqUgU6Mz2VgF2bdEGqsxfT3npa6VYM1sFklaqVdj5u2Ss6+O9VYCzO6eSGC/lLdXeQtJTuPsarlW1jHiq/Lu3pvvyPEopFW6BzGzwXAF25m5xiR073vtee8tLX3JbM1sFWkAq1SIyAxgH7DfG9PVwvwD/As4HjgM3GmNyHffdANzv2PVhY8zMQJRJxSZvQZqVnsLsSadX6FPtKfQ8hfvkkV19apmprmXE+UVSXQDraUcVbJrZKpJ4ym3XSq2nPtVO3vLSlxZ1zWwVaIFqqX4VeBZ4zcv9Y4Fujp+hwH+AoSLSHHgQGAwYwCYiC40xukSS8on7KTdvQerc7zIvlWmnqirlro+rNICmU3OfW7SrC2B/+ywq5YNX0cxWYeCpm4S3LiG+dKeoKi895bZrw4pmtgq0gFSqjTGfiUhGFbuMB14zxhggR0SaiUhbYASwxBhzCEBElgDnAbMDUS5Vt3lrPfAUpL6emnOGe96HnzGoCXT30gfPdQDNPZ++Svr6pfz4t7+zqGMDNuR+x09Nm4N9iMdjVBfA/vZZVKo6mtkqHKrrbuHaCFLTzPZlsRlPg9VdW8Odi8i4P4dmtvJVqPpUtwd2udwudGzztr0SEZkETAJIS0sLTilVVPH1lJvX/YyxfuLi4NNPYf9+6NSJrNdeI2vaNLDbYfMD8OCD1vX8fGjenJxth8sH0IzYtprbc+ZxuH5jOv92EgDOifo+emcuH/z5PvbVT2ZQ1zYMmDsDevUi6/rrmXVLdnmLiSf+9llUyk+a2Srg/M5sL3zJS2+D1Z1Tqzor8QlxwhWDO1boIuisNGtmq+qEqlLt6X1oqtheeaMxLwIvAgwePNjjPiq2+HrKzeN+JSUwYQJ8+SVcfDE8//ypB8TFwa23QnExPPQQ/O9/cPQo7NgBwHUpLTk7sTHtjuynycmf2NIijWt++RRvdTrK2tKG/GPLz1yw6TP+/MkrxN9wQcXCJCbCoEHE0YRVy2ycsAuLVm1jxq/OqjKQdWS5CjHNbBVwfmV2Lbl21fM2WN21El9cZnhj1U7m5xZWaiF/O7eQ4lK7x/u8HVczO7aEqlJdCHR0ud0B2OPYPsJt+ychKpOKUFWFkXufOF9OuVU6NdexKdx4IyxYAF27WhXqSy+F3/3OqjiPGQOtW1ut2GPHwgMPQJs27Lj9Tt74eBN9dm8hufgEh7OGktS1M+uGn8+LQ3rTJT2FwwVFHJ6ew6tDL+GLroNJP1hIi2NFtD52iCNnjuCeGQ+QOHIkA4oO87G9DICSuHje3nobPPkQWRnNPb4eOrJchZhmtvJZ0DO7lnnnnp1/uagvG/YcqTRY3VmJd3bpM0BxiZ1/Lv2ufB7tChVvt/uqO65mduwIVaV6IXCHiLyJNejliDFmr4gsBv4mIs532znAvSEqk4pAVYWRe5+4N1fv4qHxfSusjOhN+ak5Y+C3v4XXX7daoe+9F776CoYMwVb4Izklbcj+OYksABG44grrB3hveR7TD3fFnmk11109NI2/XdKPTLfjnOqj14+pizZWmBKq8OzJPL1uLkeuvI5ndgElpQzfnsuEt59j57K3OTD2HFq+/Dw0aFD+nDqyXIWBZrbySU0ye86aXUxw61rhja/dKaqq0Ltm58kSOxv2HOFvl/TzeCxn9455tkJKS63M/iLvB1bvOFRhalVnnrve5+uMJKruC9SUerOxWi9aiEgh1ujwRABjzPPA+1hTM+VhTc/0S8d9h0TkIWC146mmOgfAqNhUVRi594krsxumvLOBHm2SfQuskyfhN7+Bl16yWqXvu8+qOJ9+uk8tC9mdU0mIs04fGmCerdDjbCKuXwY92iTzz6Xf8UXeD9gNLO00mFdum8jkkV25rKCIfy79jslbz+Wyb5YwOu8rxrz5Oof3fU+zkhNw+DC8+irZnTvpyHIVUJrZKlBqktmlVXStqI3qctvXzIZTuX1ZZocKme0+tar7ffNzCytV6nU2kNgVqNk/rq7mfgNM9nLfDGBGIMqhIpsvfcyqCiP3BVwA7Mb41gpw8CBcdBGsXAn33AOPPGJVqB18aVnISk/hisEdeWPVTgxQVubbAJo7R3dn9Y5DHqfpc943v/85zO1/Dres/h/3L5tOabMUEho2gNNOI+vss1l1soyiYjtHnn2BAdriofykma18EYzMNgSu9daXeaaDldklpXbi48Rq2S6rPAOVzgYSm3RFRRUSvvYxq26e6b9c1JflW/az7Nv9GGNIcg3xH36A1FRrgOHHH7N1/zFWJaRyWuIJejz4e6u/9Jw5cOWVlY7ra8vCpZkdmJ9bWKMWiKoC1nmfs/Vj+mkX813LdM6aMIabR3SHJ5+EefNomphI023b4KYrYNky6/dUSqkg8TeznS4f3JEfjp4E4JPvDlBWVvUKhzWpiPqS28HK7Jz8g+w5fILZX+30WKnX2UBik1gNEtFl8ODBZs2aNeEuhqqBacvzePKjLdgNxAvcdU4Psjun+hSgnsIdqPjYjz6yBhWefz78+CN89lmF5yhu05akOW/CL35R5XF8LY+n/fwZ7e38HZ3B7/ELbMkSuPBC6NMHPv4YmjWr0TFUZBARmzFmcLjLEUqa2dHHn8wGH3O7mv397VNd3T7+ztDhU26rqFeTzNaWahUS7i0KKQ2TfA5Qb0uHl++/ezdMnAjt28PixSDCsrsfYdr+JLrtLyABQ4c7b+f2X1RajRmoGKyeBj16WrUxUF8ITj6dLhwzBt5+25oCcORIa6q/9HSfj6GUUr7yJ7PBh9z2YX9fK8me8jjYmQ266IuqTCvVKiTcw+ft3MLy6YuqC9AqT/GVlMBVV8GJE/D551BaCmVlNG2WxsbpOXzdrhdxccLUlCYen7u6YPU1eAMx2tvb6cIKXxDnnw8LF1q/c2YmPPoo3HwzxMf7dAydO1Up5Qt/MhtqPljPdXYNESGlYZLH/SIps8Fzbgc6ZzW3o4dWqlXQuM9P6gwfW0ERc9fsKl8xIj6+6sD12hpgDPzxj7BiBcyaBT17nnoMMGVcH6a8swG7MUxdtNHjLCFVBavNMTuHL8Hr6QskEEHo8QvivPNg9Wq46Sa47TZ46y2YN6/a7iA6d6pSqiqBymyoeStuVnqK35kN+Fz5D2lm+5GzmtvRRSvVKijc5yedaytk9q1WGOTkH6TUbm0X4PKsWsxZeuAA/PKX8N578OtfwzXXVHpM0fFi7MZUWSH21priDDJnOMcJVba2uH+BAAEJQq9fIN26Wf3GZ8yA22+HrCy46y64/npITq7ZcymlYl6gMxtqPljPn8x2/g6+Vv5Dntm1pLkdXeLCXQBVd9gKipi2PK/8v33X+UmdYQCnQjFeoF5iHJdldqjZgZYtgwEDrIF7//43PPusx91cj+OtQuwM1rvO6VEhRJ1BZrA+JMO6tqg2ZLPSU8r7DHoKwqq4vnau23YfPkFCvJffQcTq+rF0qTUbyB13WP3K//3vWr8eSqnYEbLM9pE/mQ3UuPIf6MwGSGmYRJxItQ0xvtLcji7aUq0Cwv0U1ZRxfSrMT+o+12etBneUlcGUKVY/4u7d4YMPrMq1F74ex1NrintriLflaL2pSX9Cb6PkndsS4oSrhqR5X4XsrLOsVSFXrbJenzvvhN69YfToWr0eSqm6LySZXUP+ZDZUzt2aVP79zWxnN5mpizZSZjfExwlTxvXx+7XS3I4uWqlWAeH+X37R8WJmTzq9Uv88pxrP4XnsGFx7Lbzz/+2dd5hU5fXHP2d2WYogrIBIW7ooxbKLFI2KiiKGCIIVCyYo6g81aqwhEkVsMSoaiRHB2BEFFCQasYAdhF0LxQIiCytFhUUwIFvm/f3xzizD7NSddmfmfJ5nnp29c2fuuXfu/c6573vKXBtL/PDDsN9+Yd9W11qhsQpZNO8PNkLiXVbtNrRp1jC8Df362eogRx1lj9WKFdCiRS27VJQVRUm4ZteRWLYTi27Hqtm+o90GMMZQvquiTvsRyDbV7fRAnWolLgS6y/cVAu9UWX6jPMp3VUQneK+8AuPGwebN1pm+6qoE7sleYhWySN8fbIQk2ja33incgQ8+Ts/fnWDDQV54oc72K4qSuUSq2V7tSZeR0lid8lg0O9qKJ1rVI/PQ5i9K3AhVYN8/6S/iRJBt26BdOxvu8eijMGBAnWxwOoHsjqapQX6jPCbOX1kzHfn2rx/S9u932vrd554LQ4cmc3eUEGjzF8UphNNsb/gZIrVacSfaBqdT14YywTRbq3o4F23+oqSEYHf5vlNiQFRZzGV/e5h2u3ez8t4p9IzAoU7X0kOBjl24URPf/XWJ1GTN76l082jfEUwaXQbz59tyg7NmwciRIT8rHX/YFEWpO+E0223wJC+aiOtTR6MlmabZoZZDaM2eXVIW1b6rZjsTdaqVhONb1N9NmPJ0P/8Mo0dDhw6sPuJoGv9zCh91OIw/fLCD5w4pr1OcW7oRSix9RzleX7GpZn8xBhG7jgFe/HwLZ9z+IHLLXbQZ8VsOPP8CXAcdZFucf/65bdcuUlOXdlZxWcJGohRFSS98wxhyPCPV1dV1T+ALRjZptn8DHX/NnlVcVpNYGc5Zfn7J+pp63qrZziIuTrWInAo8BOQA04wx9/i9/iBwguffRsCBxphmnteqgeWe19YbY06Ph02Kc/BNAKkVU717N9x1F/TowcoWHWj8lz9TUPwBkpNDtwpbGu7Pg8clpIOXEwn1o+QfRiOwTzjNcd1a8uaqLRigutqOfMwpKaPJSdfz4uabKDj1VHKaNrVt3S+4gJLx93D+c1/sFXnS+4dNiRzVbCUUgWo4R+I0RuskZ4tme8No3FCjtbm5LgYeHFizQ92UFJeWM2HuiprSgRWq2Y4iZqdaRHKAKcDJQBmwVETmGWNWedcxxlzrs/5VwJE+H7HbGHNErHYoyaMu005Bp8QeewwmTQKgp2fRhNOuYsSdf6TB2jVcv+A7vsxvH5HgpkPpoXDHLtSPkn8YjW8N7WsGHQzAe6t/rPmBEqzg/tioGReceyf/mftXmu3fCM4+GyZPpknZNir6XYHBDpcIWgc1G1DNzj7iodnB4oP9y/JF4ySng2ZD6OMXiWa7DTWlCmFvDe2Rhe0Canaom5LFa7dS7d77WS4R1WwHEY+R6r7AGmPMWgAReQEYBqwKsv55wF/jsF0lBcQ1Bq6yEh54AI49lpfO/SPvLljK1oZN+aTjYbQqN4wbMZjbi6L7MXBy6aFIjl2okZv+nZuT65J9xDk318WQXq1rjpH/6NLskjIqq9xsPeBA1i5cQmGXluBywYEH0u2WW3jwp9003bWDnlu+ZUenblTceTeHOvT4KXFDNTuLSGTccqCyfNE6yU7WbAh//KLVbJdArks8QxkE1exgNyX9Ozenfj0bTulyCROH9XL08cs24uFUtwU2+PxfBvQLtKKIdAA6Ae/4LG4gIsuAKuAeY8wrcbBJSRCRTO9FPCry7LOwYQM8+iiVLXrz37KGNTFi+Y3yaso5jTuha4L3KjlEcuxCjdwUdcjnrD7teX7J+prwj+MPblkrg9z3ePl+VqHvtm68Ed5/n9P/+1+2degKJ59Mlw8XwZlD4MMPba1rJVNRzc4iIg3JiHY027fja3W1jb3euH03QMZoNoQ/ftFotgvo3bYpX27eyYxP1jO7pCykZgdLhEyH0f1sJR5OtQRYFqxO37nALGNMtc+yAmPMRhHpDLwjIsuNMd/W2ojIWGAsQEFBQaw2K3UkXAxcxKMin3xi60337UtxzwFMnL6kpgvVxQM6ZmSpoUjjB0ON3IwobLfPSMaBTeqHFfyAn+Vywbx5yJ49NG/UyC7buhWOPBLOOw8+/RSaNIl5nxVHopqdRUSiO9GOZvvHCp90aCsWffPjPo5iJmg2RHb8otHsXm2bsvz7n6PX7Ai3p6SWeDjVZUB7n//bARuDrHsuMM53gTFmo+fvWhFZhI3dqyXQxpipwFSwNU9jtlqpE+HukiMaFfnmG/jtb+HAA+GVV1j81bZ9ulCt3LQjIzLC/YnHCEOgBKJw04VByckBr0MN0Ly5Lb83cKBtHPPUU1Hbp6QFqtlZRCS6E22Coe/61W7D7spqqqozT7Mh/t11IQbNVhxPPJzqpUA3EekEfI8V4VH+K4lIdyAf+NhnWT6wyxizR0RaAMcAf4uDTUoCCXWXHPauvrQUBg8GEXjjDWjdmv4V5fu8Z0iv1ixdty0jRSceIwz+nxHXqcBjj4Vbb4Xbb4fCQhg7Fho2jO0zFaehmp1lhNOdaKtw+K+fyZoN8e+uq+EbmUtcOiqKyGnAZGx5pieMMXeKyERgmTFmnmed24AGxpibfd53NPAY2PLFwGRjzPRw29PuXM4maGzeggW2u191Nbz9NvTpE/Q9Wtg+hVRVwaBB8O67NgRk2jRbMUSJC07oqKiarfhTl5hq1WwlG4hGs7VNuVInohbQzZvhkEOgoADmzIGumZPIkins85223g8WLoSJE+Gjj+C662w98fr1U21m2uMEpzrZqGY7A3V8Mw/9ThOPtilXYiZchyj/2qTehi4QpEnAn/5kG73MmqUOtQMINMpUK1Fp8GAbX33ddbb04XvvwfvvQ4MGqTZfURQ/wjlX/smFZ/Vpz4gIO/gpqSfQ95vObd4zFXWqlVqEu1D3KWhf6a5pl5rraWdbq931lCnw/PMwYQIcfHAK90yBwN9voEQl8PzY3jiJouOPh3POsbHWd9+d4j1QFMWXSJwr/0Ykzy9Zz0vLNgTWbMVRBPt+Q+q23iSlBHWqlVrMKSmraV0dKJPbN0lFRHAbYy/qagOYfd/34eu2ksTpp8P48anapayiLl0b/ROP8hvl+Yn4yRSNGQP33QdnnWWTGBVFcQThNBv26rZ3PUMQzVZHLCUE1e3qalZ8vJy2P2yAajd5ppo1b1RQ1KM5p5Ru5JvVn/OL1GNPw0Z03iDcMmsT2131md6oEY9ffjxFnTIradTpqFOdZUQyRfjSsg01RWtzcmpncvuWCMpvlMfE+SuprLLF/xGhuto6Zr/Z3w0jr4RjjoEXX4S8vCTsYXYTasTK+93nN8qrlenvX/YpYImtv/8d5s6Fm26CN99M8Z4qSvYQLhwvnGbDXt2eXVLGrOKymoYtvpqdaVU7Uo7bbev/b94MW7bYR4Dne37aRuudvzLCuMkxhsoGudQTQ2W1G9fOHYyurGS07+c+Yf90Ax7yXf4MDPHd/N9d0KaNzWXyfbRqBfvvD02bQufO0LJloo9E1qBOdRYR6RRhldvKswBnFrUL2tXJu7z7QU32qcHpfX74X66GX36x1SM0wS0pBKs3+/yS9TVhOv5x8N7v0b/sU60SW82a2dmGa6+Ft96yFUIURUkokYTjRaLZsPcaH1nYLqBm6yh1DPzwAyxeDF99ZXsxfPYZfPEFVFbWXrd+fevYtmoFBQV816ozyzftpFpcIELv9s3Iq1ePJaXl/JLXkO8PaMOQvp3ZWQ1dWzely0FNoXFj6xg3agS7dsHOnXz77SamvvY59X/dRePqPVx4aD6td/4E69fD0qW2SEBFRW172rWzjb+OPBJ69IAuXezznJzEH7cMQ53qLCKSAv/+YQAjPYksoUZK/J2xog751ul69llb8/iQQxK/cwoQuN5scWk5E+auqPnhrahyU76rImQr4aANDy6/HB58EEaPhmeegRNPTMZuKUrWEk63g2k2BNftgJqtRMavv0JJCaxaBV9/vffxzTd71znwQOjZE665Btq3h4MO2utEt2plR4hlb2PT/5WWc+u0xTXf4YShPa1mH2w12yVw0HHdw7Z/7zIIzj51aM133tr/e3W77Qj5Tz/Bjh1QXm5vAj791D7+8x+7DkCLFtC7N3TvbnX+xBNtgzAlJFpSL4vwjnh4L9xgSSm+QgzUTBdGnMzy669w2GFgDCxfrtUikoxvmEf5rgo2bt/N80vW10wP57qEmZcNqPsP6eef26TFr7+GCy+EW26BQw+Nm/2ZjpbUU6IhEt3212zf0DytDBEjmzbZsqLeR3Hx3tHn+vWhWzfreBYW2mpJPXrYWb0oKS4tZ3ZJGV5XO66aHSn/+x9895393f7vf2HNGvt85057E9C3LwwfDmecYfc5S9A61UpQAolvuBJM3sQWgByB604Jcce8cyeMGGFHqt98U0MEUoR/+SxEqKpy43IJE4f1YlS/gtg2sGsX3HGHLbVXUWGd6rPPhhtv3Lf1uVILdaqVaPG/UY6kbJ7LJ4k8rG4rlooK60QuXrzXiV63zr7WoAEcdRQcfTQMGACHH25HoeMUIpFwza4rVVU2dOTNN2HePHtTAbaS19ChcNpp9nhksO5rnWolKN5pv2hKMHkdaoHQySwzZ9pRy/Xr4d//Voc6hfhOGVe7Def0bU/bZg3rFDfpO4IyotATr9mokS2td/XVNk5v9mxbbm/PHi25pyhxxnvNRlM2D2NwuQTBaBJiMLwO4xtv2IGgZcushgG0bm2T7K++2jrSRx6Z0GT7hGt2XcnNtU7zgAG2LO769TZhff58eOQRO7CSmwvHHms7Jo8cmdVhIupUZynRxlfn+DQLCHiBLlpkL6gjjrAtyI8/Pjk7ogQkUJxlqCnjYKJbXFrOeVM/pqLa3lq9VFzGjEt9fsxbt4Zx4+zjggvgoYdsCcW2bRO6f4qSbdQlJyZQQnLWs2OHHQCaMcM61L/8YkMbjjrK6li/fvZRULBP3HOiSZpmx0pBAVx1lX388gu8+65tCjZnDlx2mT2GgwfDeefZUrpNmsRnu2mCOtVZir/DvHH7bopLy2slrwRMVvOhuLScxd/+xOjrb6Bx27Z2uqxhw2TuihKAcN9dpJ24Fq/d6qllawn0Y+4V+nYjL2fozBfZdsOfafn8U4nbOUXJQuKq2dlU7cM3fGHBAliyxC7r0cMmXB97rJ1VTfHoaio0O1w4UVgaN4bf/tY+7r7bJjvOmAEvvGCTHhs2tCEi550HQ4ZkRX6VOtVZin/N0hmfrGd2SVmtC9U/S9wX70U+rPi/NP50GevumUxHdagdQ6jvLtSol++Pbv/OzamXIzWjHv7TyP5x99sOP5WLXniWFWP+j14n9Uv4PipKthBPzc745MUtW+yM6fz58PrrsH27HXUuKoIbbrAjqP36JXUkOhJSodkuIT7ngohN1iwshHvvtQNsM2bASy/Zx/7723yryy+3xz5DUac6A4h05MF/PW+b06rqvS3HJ7/1DdcMOjj8xfXzz1TeMYn7P/yE3371Acva9eCTosH8X5z3TUkMgUrvQeAf3RljBwSNz/OPu//H0edy5vK3aHjbrXDSguTvmKKkCZFO5YfS7MoqN7NLyiIeeY4khCQt+d//bBLdvHk2ydCbXNiypa1WMWQInHRSykejYyFRmp2Qc8Hlgt/8xj4eegjeecc62LNnw5NP2lj1P/0Jfvc7G4+dQcRlb0TkVGxjnxxgmjHmHr/XLwbuA773LHrEGDPN89po4C+e5ZOMMTpvHAX+TT1ClcnzvfC8sXbe7noVlW7cwIdrfmLpum0BO/HViHZ5OQweTP+lS/lxv3ymHzWMyYP+wJPdWiV575W6EmyqMdCP7rgTugaN7du5uxKXCMbYVsfb92vK9AFncs27z8Dvfw8PP5x1MXXpgup2avAmkYUrUxpOs71hIIE+J5jDHswxS0s2btybMPfOO7aU60EHWUfuyivt36OOsg5eBhCrZoM9pz7bsB0RQTya7ZIwBQhiJTcXTjnFPh5+GJ54wvY6GDHC1uy+8EK45JKMKdEXs1MtIjnAFOBkoAxYKiLzjDGr/FadaYy50u+9BwB/BfoABij2vLc8VruygUBNPYLdbfpeeBWV7lrd9V5fsYkP1/xU66611l3wmH4UjT3X1ip+9VXW9z6GX9du5clsic/LIAJNNXp/dCsq3YgI+Y1qZ7v7Tx8KkOMSLvlNJ5o0rEf/S+6HpzrCpEk2ru6BB2wSo+IYVLdTQ6AypcFGCcNptrcG/YxP1u+j2xC8SkgkMdeO5uefbULcc89ZR9oY2/3vssvsiPRxx2WMEx2Iumo21E5gzHEJl3o1O1nnQpMm8Mc/2mTG116zVcImT4b774dRo+C226Brepd9jMfZ1xdYY4xZa4ypAF4AhkX43sHAm8aYbR5BfhM4NQ421aK4tJwpC9dQXJo5ur947Vaq3XsTElwiQe828xvl4RLBJeBy7a1fWunprnfNoIPJy3WR43fX6n8X/M0jT8Cbb7J+/B0wdChFHfKD3hUr6UdRh3wmDO1Zc45MnL+y1jXjP31oq3cZmjSsZ8+FLi1h4kQ7Ddu5M4wZA5s3J31flJA4XrczVbMjLVMaTrPHndCVEYXtaum2v2bPLinb5zimnWZXVNgR6bPPtiPRf/iDDe+49Vbb1XD1auuYDRyY0Q51MCLRbKidwOh2+2h2ss+F3Fwb1/7yy1BWZvsbzJljuy9feqkt25emxOMMbAts8Pm/zLPMn5Ei8oWIzBKR9lG+NyY+L/6GTb89g4+nzuT8aYszRqT7d25O/XouXNiOSxOH9Qo65TNx/kqq3QaX2BFFfyH2jmBcd0r3fUY2+nduTl6OcMFnr/Ha9HGc/vCtrGjVhVP39MiY46jsS/muin1+wL2jX168IyNe8Qg6fdi3r21lXlkJjz6aHOOVSHG0bheXlrPqrIvZdO+DnP/4xxmjNd5rJ0cgL0cY1a8gaOhHOM0GAuq27za84SH3L/g6vX77KittmdbLL7eO9PDh9v9LLrE366tX27r4hx7quGTDVBBOs4GaBEYvjgn/adUK7rkH1q61I9hPP227VF51le1mmWbEI6Y60Bnt36bxVWCGMWaPiFwOPAWcGOF77UZExgJjAQoKousstGTLbmflfIEAACAASURBVMaufJdVzTvwcccjMiI5wxszF0kdUt/REe+IYqApwFpTS7t3UzT73yx5bz5N31/I+m69WXBgZx7pfzZ73JIRx1GpTbi4S98p5LAlmbp1syWVHn0UzjrLlrHKwtEkB5Jw3Y5Fsz/5aiM9f9rAhUvnMfDbZXxe+DBFHTKjYsCIwnZhm3JEqtlQW7d9r89A4SGO1ezNm22ljtdes6XvduywTaaGD4fzz4eTT4Z69VJtpSOJJFa+qEN+yATGlHPQQTap8frrbejgv/4F06fb+PibbkqbJNN4ONVlQHuf/9sBG31XMMb43jY9Dtzr896Bfu9dFGgjxpipwFSwLW+jMbCoZwE/NM6n4/ZNzrk7i4FoyyIFuuBCle4BbBLIsGGwbBlNO3eGSZP4cdTl3PLEJ5mR5KIEJZK4y7Dnjy833AAnnAC9e1vHeuZMHV1KPQnX7Vg0u+8hbbhg1B2MWjyXGxc9ieuy30GDJ21L5DTFX7dHFLYLum6dNNsH3865s0vKnKnZxtiW14sWwX//uzdGuk0bG+oxZIhNbmvcONWWOp5IY+Wj0u1U0b49PPaYDQm5/Xb4+9+tg33ttXDdddC0aaotDI0xJqYH1jFfC3QC8oDPgZ5+67T2eX4GsNjz/ADgOyDf8/gOOCDcNouKiky07Diqvyk77CizbN22qN/rNB55Z7XpdPN80+Gm+abzzfPNI++sDvueZeu2mUfeWR3Z/u/ZY0xhoTGNGxszb17dP0dRvKxZYzZdOs4YMGvv/2eqrUkpwDITo+7G+ki2btdFs71as+K/HxjTq5cxYMy4ccbs2hX1ZzmBaHU7XlrrOM3eutWYRx4x5sgj7XcKxnTrZsyECcZ89pkxbneqLVQ8OOLcWbnSmDPPtOdJixbGPPywMRUVSTUhGs2OeaTaGFMlIlcCb2BLMz1hjFkpIhM9hswDrhaR04EqYBtwsee920TkDmCp5+MmGmO2xWpTIJr0PIQmCxbQ1ul3aREQaqonWCmlqO5QJ0yAkhJ45RVbR9KHtLjTVRJGtN3YfDt3TWp5Kk+2W8Qhf76BzwcO4vDCbkmwWAlEOuj2Xq3pCscvhVtusQlpCxfa6g9HHBHvTSaUUHWGY9bsEDhCs3futJWAZs2CV1+1yYeHH27DwkaMgAMPTK19GUwsmj1x/srUNwrq0cM2jykpsbOeV18N//iHbTAzfLjjZj3FOuHpRZ8+fcyyZcuie9Ndd8H48bZX/X77JcawJBLoQomlW5b38wZt+ZLuo4bZhJCpUxO5C0qaEe355bu+S2xmeqcfN7DgiXEsP/NijnhxehKtdw4iUmyM6ZNqO5JJnTQ7EAsWwMUXw08/WU2/7rq0itH31+14aLZjS+Nt3Wod6NmzbYvwPXtsUtp559n24Gl2U5SOxEOz3QZyBK47pTvjTkhxuTtjbNz9DTfYyi8DB9qyfB07JnSz0Wh2+qhRrHhrH65dm1o74kSgskiBisBHgvdCmj53KfuP/QO/duxsi7Mrig/Rnl9zSsrYU2nXd3uqGKxr2Z65vU/ksLnP2VJKihINp5wCX3xhk19vuMEmr6XReeSv27FqtuOqevz6q42RHjnSOtC//739vq64At5/H77/3v62qEOdFOKh2f4VZ1KKiM2r+Pxz+Oc/bUz+YYdZx9ohA8TZ51SvWZNaOxKIbymlaC6CL98r5qQV7zH3yWtp/r/tvHrD3zJiNF+JL9GcX8Wl5by0bENNSYjcXBcTh/XiulO6023K33G5q21TGEWJlhYt7OjntGm2vNphh9lEpsrKVFsWNXXV7Lo643HH7YZPP4Wbb7YlNPff3yYlL1xoZxGWLrU1pR980HY4zMlJjZ1ZSrw0O2WhH8HIzbU3al98AYWFtnb5GWfADz+k2rL4tClPC7p0sX9Xr06tHQlmRGE7ftq5h5ZN6kf2hsmTueDaa7kAKNv/QC686G/ceNoJCbVRSU+i6ca2eO3Wmk6fApxZ1I5R/XzKqp15pm1XO3GiZvcr0SNimwodd5wNVbviCuu43XOPI+MsQ+EtsdezTdMa5zicA5PSduPbt9uydy+/DG+9Zf/PzYWjj7aOdL9+dgZBr+uUE1fNdiIdO9qqMQ8+CH/+M/TqBY8/biuXpYjscaqbNoWWLdN+pDpYHJ1/+1uXwOySstB3mI89ZsvUjBjBlxddwXt5rbixh8NqVyqOItKkJ/8f/ZH+5cOuvhpeeME2h7niigRZq2Q83brZcINXX7W1bEeMsDds//iHrXvrAMJpdkWVm1yX8JKUUVUdWexr0tuNb9xouxq+8op1Yqqq7PEdMcLe2Pz2t3YGQXEccdNsp+JywZ/+BIMHw4UX2pvqMWNsUnMKbuyyJ1ER7J10gwZWFNKQUEkHUxau4f4FX+PTtTx0csEXX8BRR8GJJ8K8eVpUX4k7IROpjLHTxVu32nMxi0a1NFExQVRV2Zq2EybYH9qLL7Z1blu1Sux2QxCpZnvH1Q0OSgr7+mvrRL/8MixZYpd162an2YcPtyPSaZQkqoTH8cmv4aiogNtuszNWXbrYKkF9+8b8sZqoGIyuXdN6pDpUHF3EraPBZs6fcw4ccIBtCaoOtRIFxaXlTFm4JmxyVKBk2hpE4P77bbzltdcmxlAlu8jNtbG9K1dah3r6dOsE3nQTrF+fEpMi0ewcgXo5Qr06xFbHFWNsDPT48baM2SGH2ONZVWU73K1YYR3te++FAQPUoU4j4qLZ6UBenq0KtGiRdbCPPtqeu9XVSTMhe8I/wDrVzzwDu3dDw4aptiZqQsXRRdw6evt2G++2bp0tTdOyZXJ3QklrYikBVovjjrMOzz332Ju8QYPia6ySnXTrZhMX//Qn+Mtf7Oj1fffZBLoLL7RdPZOUiB2pZnuXJ3WU0O22TvT779uEz48+gk2bbDLh8cfbsKzhw22HOyVtiatmpwvHHWcrhIwbB7feajt2PvMMdOqU8E1nn1MN8N139k48zQgXR+cfO+W9O91n3dtvh+XLbSH+gQOTaL2SCQQaeYtJoG+7zcZW33gjLFumo19K/OjWDWbOtAMITz1lf1R//3ubTDdmjK2XfOSRCU1qjFazgYiTFaPCGPu79+GHNtxq1SrbTGPzZvt6587292DwYFuusLkDyqcpcSHump0uNGtmwz9OOw3+7/9ss6GHHrKzWAm85rPTqV6zJi2daog86cA3cTHHJUwc1otRbV22g9Xo0VY8FSVK4l51oH59Oz13wQW21uiYMfExVFG8dOwIf/2rjbX+4ANbKWDyZDuC3bWrnSU5+2zo3TshP7YxaXa01Rfcbvj2W1vl6rvvbF+Gb7+1N6zff2/XycuzoR0nnghDhtja39rRMGNJaaUYJ3D++bac4+jRtvReVRVcemnCNpe9TnWGs3jt1ppKIFVuw4S5Kzjl2+dp4Xbb6RBFqQMJqTpw3nl2un7sWNt17Yor0qokmpImiMCxx9rH1q02AW/mTLj7brjzTutonnOOfRx6aNLNC6TZ3Q9qEvga27XLNr3ZuBE2bIDPPrONMD79FHbs2LtegwZ2FPo3v7FT4scea/ctN7t++rOZpFeKcSIdOtgCFdOmWSc7gWTXlXXAAZCfn3ZOdaDWtuEukP6dm5Pjkpq6kwO/WUyL2c/ZLmQJbumpZDaRjrxFjMtl4/vPPtvGwD39tA0J0fNUSRTNm9v61pdcYhtGzJ4NL75o66bffrsdtT77bOuEdugAbdtGldAdD812G0PJF99R9MMam3y5atXev+vW7fvmBg3s9PYFF9hmGIceap3pVq30BlWJv2anIy6XHbhJMNlVUg9seZVmzWDBgvgaFQcCCa9/ksGEoT2ZOH9lREkHzy9Zz4S5K2i+cyuvP3ElDTp2oFHJUjvlrigJIKaSTNXV1qG+5hpb7vHNNzPSIdCSeg5m0yaYNcuOYH/44d7lLhe0aWMd7E6dbBfHtm2hYUNW76xm5U+/cnCPjvQ4+jCKy6uDavZ+Us3zp3Wgd/V2W5Fk/XrYssWOnG/bxpYftrPhhx00qNxDu5+30OzXX/baUL8+dO8OPXtap7lTJ2jd2trRpYtWcVLqRNqX0UsC0Wh2do1Ugw0BWbw41VbUIliGrn+SwesrNkWcdDCqXwHdD9yPlmcPp5m7EtesmepQKwkj5pjQnBybSLZ7tx2xfuEFGxqiKMmidWu46ir72LjRjgyXllrnt7TUPhYtgmefrXlLN8/DS88mTVlILpWuXHY0aMwB06oYtHMHTfbsomHVHrjbb5tNm9qR8wMOoNV+DWnYoSXbqoWKrsfD4Ydah7lnTzvyrGEbShzxb0B0Vp/2jCjUBnCxkH1XaNeudhSiosImbDiEYBm6/kkGQ3q1Zum6beGTDqqq4JVXKJo+HZZ9YBMUUxAnqGQPUcWEhuKyy+DJJ+HKK22d0Q4dEmGuooSmTRv7CMTWrbB1KzPf/YqX3vuGelWVNP91B6MOdNN191Y+/GIDOVWV5O/5hSYdDuSDrVX8nLcf/2u4H8N/exQdjzwUCgpsuTq/8q77ex6Kkmh8/Y6KasPzS9aH78SshCQuTrWInAo8BOQA04wx9/i9fh1wCVAF/Aj8wRhT6nmtGljuWXW9Meb0eNgUlG7dbIb02rU2McUh+DrPOS5h4/bdFJeWB0wy6H5Qk9DTNR98YGPrSkvtj8Ltt1tHRVESSKCY0DqVb8rJsaWQ+vSxNYXff19nWOJMWmm2E2neHJo3p2v9lqxYX4+KSjcul3D0sF4c3a+ATp4p9YLOzenQIZ+fPP8f27k5HdVZURyC1+/wDoYYsqzsXgKIOaZaRHKAb4CTgTJgKXCeMWaVzzonAEuMMbtE5ApgoDHmHM9rvxhjoupRHFN8XkkJFBXZqeVzzqnbZySI4tJyZpeUMau4jKrqOhZq/89/4Mwz7QjIfffZmqM5OYkzWlF88Mbxu40JeP5GFb83Zw6MHGlrjE6ZkmDLk0eqY6rTTrMdTrhzXlGcjK/fUV1tZ8Bj0u0MJNkx1X2BNcaYtZ6NvwAMA2oE2hiz0Gf9xcAFcdhu3ejVy4Z9lJQ4zqn2xlBXVdexUPuqVXafevWy1RRatEiswYrix6h+BUFnUiLt7FUj4EUnUHT99bae8DHHwKhRydyVTCa9NNvhlO+qwG1M9jXXUDICb2WQkYXt6qzb2e50+xIPp7otsMHn/zKgX4j1xwCv+/zfQESWYacZ7zHGvBIHm4KTl2fLJRUXJ3QzdaXOhdp//tmOUO+3H8ydqw61kjKClW+KpLNXLQG//EaKliyxxfoPP9wmbCmxkl6a7XCyvrmGkhHUVbezsg16COLhVAeqeRUwpkRELgD6AMf7LC4wxmwUkc7AOyKy3BjzbYD3jgXGAhQURNllyp/CQls2yRjHleyqU6H2ykrrUK9ebcuQBUuuUZQUEonzUUvA1/9M0cyZtp30yJGwdCk0aZIC6zOK9NNsB6PNNZRMJpxuZ20b9CDEw6kuA9r7/N8O2Oi/kogMAsYDxxtj9niXG2M2ev6uFZFFwJFALYE2xkwFpoKNz4vJ4qIiePxxW0C/U6eYPioRRF2o/W9/g7fesm2eBw5MmF2KEgvhnI/i0nK+376b3BxXTWxf/87NoXW+zYE46STbrOOFFxx3M5xmpJ9mOxxtrqFkKqF0O6hmZzHxcKqXAt1EpBPwPXAusE/wo4gcCTwGnGqM+cFneT6wyxizR0RaAMcAf4uDTaEpLLR/S0oc41TXOSapogIeeQSGDIGLL06YfYoSD4I5H/71Us/tW7BvvdSBA+Guu+Dmm22ZvT/+MbmGZxbpp9kORONIlWwhkG6H1ewsJWan2hhTJSJXAm9gyzM9YYxZKSITgWXGmHnAfUBj4CWxI0zeMkyHAo+JiBtwYePzVgXcUDzp3dsW0S8utlPKKSamRIAXX4TNm9XJUNIa3ynEarehTbOGtcX5xhvh44/h+uttx8Wjj06NsWlOWmq2w4g66VYdbyXDiEizs5C41Kk2xrwGvOa3bILP80FB3vcR0DseNkRFgwY24amkJOmbDkSdEwGMgYcesvW2TzklhXugKHXD63TkN8oLn+wlYpvCFBXBhRfabncNGiTd5kwg7TTbYdQp6TbLE7iUzEHDPoKTfR0VvRQVwbx5jkhWrHMiwMcfw7Jl8M9/pnwfFCUU/iN2gWqyTxjak/JdFaFH9Zo1g6lTYdAgW4f91luTuyOKQh2TbrM8gUtJLwJptncAZOL8lRr2EYTsdaoLC+GJJ6CszDZKSQLBpgLDJXAFFfCHHrJOxkUXJcV+RakL/iN2E4b2ZOL8lTVdvMA6HeW7Khh3QtfwH3jSSbbT4l132VJ7Bx2UUPuV7KWumg1aak9JX4JpdkWVG5dITV12DfuoTfY61UVF9m9xcVKc6nBTgaGyxwMK+IYNMHs2XHutrU2tKA7Ff8Tu9RWbqKja61ALRO903HknvPQSTJ8O48cnwmwly4lFs72va6k9JR0JptluAxiDyyUIRm8WA5C9TvVhh4HLZeOqhw9P+OYCTQV6l0ciuLUE/J//tKErV16ZSLMVJWb8R+yG9GrN0nXbqKxyk+MSzurTPvrpw27dbAjI1Km2IkhOTuJ2QMlKYtVs0FJ7SnoSSrPrRRqql6Vkr1PdqBH06JG0zor+J+nO3ZWc89jHuI0JOAoSMmt81y547DF7M9ChQ1LsV5S6EmjELlgr86i44gpbvWf+fBg2LL5GK1lPXDVbUdKIhGl2FiDGpF9N/j59+phly5bF/kGjR8OCBbBpU+yfFQHe5Kyfdu7h7a9+oNptj71L4E+ndK+JJw2bNT51Klx2Gbz7Lhx3XFJsVxTHUVkJhx4K1dXw2WfQtGmqLYoIESk2xvRJtR3JJG6anWS8jvLO3ZVMfX8tHsmOXrMVRUlbotFsV6KNcTRFRbbG88ZazcQSxpySMt5ctaXGoQZwiewTlxRs2hGwNwDjx0PfvnDssUmzW1GSTXFpOVMWrqG4tDzwCvXqwbPP2vyCiy6CHTuSa6CS8RR1yKd/5+ZM++A7fCQ7Os1WlCwirG5nONntVPt2VkwCXuH1nRvIdQkTh/XaZ1TDO+2YI5DjEjZu321PUGOs8/C//9l6vVpGT8kQ/IXYO/J3/4KvOX/a4uAC3b8/3H8/vPqqrT3/5ZdJtFrJBhav3eo3CELkmq0oGUog5zli3c5gstupPuII65gmOa46RyAvRzi/XwEzLxvAqH4F+6znjWc6p28BiDDjk/WcP20xq596Cd56y9bnPfTQpNisKIkmkBBHNfL3xz/amu179sDZZ8Pu3ckzXsl4+nduTv16LlzYQZBJw3tHrNnZ6FQomU8w51lnbLI5URGgcWPo3j1pI9XRlFgq6pDP4rVbqar2nKCV1TS5724oKICxY5Nir6IkEq/zvHH77lpCnN8oD5cIYMhxCZ9v2M74l5cHrxLSrx88/TQMGQJ//jM8+GDS90fJTCLV7VqarQ1flAwjlGYDNV0Wq6rcAHy+YTvFpeVZdQ1kt1MNNq560aLkbS6KEku+2edDv/mIg1Z9Zkvp1auXYCsVJbH4JnblumSfdrfejl3VboNLoNoYFqzaAsBLxWXMuDRIEtipp8Ill9hr5MYboXXrJO+VkqlEqtva8EXJVMJptvc1lwAC1QYWrNrCom9+DK7ZGUh2h3+Ajav+/nvYsiXVltTCO0IyfU8Jk1+519r6+9+n2ixFiRnfacJqt+HMonZcd0p3nrukP+W7KmpyD+zre98Xdkrx5puhqkpHqpWU4NVs77mcLY6EkvlEotlevfZN6s22MBAdqfZ2ViwpsVPHDqPI/AxTJsHJJ8OsWdCgQapNUpSY8R/RG+kX1uF9LccluIGqaqvSYUf/unSBc86BRx+FW26BfHVqlOSiDV+UTCRhmp1hxKVOtYicCjwE5ADTjDH3+L1eH3gaKAK2AucYY9Z5XrsFGANUA1cbY94It7241jz9+Wdo1gwmTXJmu+MLLrDtyFevhnbtUm2NosSNUM0yfF8DmF1ShgA92zRlxcafEQgeX/3FF3D44TBxItx6a+J3JEqcUqc6mbqdrnWqFUXZS8I02+FEo9kxO9UikgN8A5wMlAFLgfOMMat81vk/4DBjzOUici5whjHmHBHpAcwA+gJtgLeAg40x1aG2GXeBPvhg6NUL5syJ32dGQcAT9bvvrKP/xBNw001wzz2hP0RRMpzi0nLOm/oxFZ4RkLxc1z6xevtcR1deBB99BKWlNiHZQTjBqU62bmeaU63dExUlPFFptoOvo2g0Ox7hH32BNcaYtZ6NvwAMA1b5rDMMuM3zfBbwiIiIZ/kLxpg9wHcissbzeR/Hwa7IKSyExYuTukkvATtxrfgIzj8fKipg3DhHjrYpSrJZvHYrldV7BwF8qyv4X0ev/P5KDpk/Hx5/HK69NoVWO5b01+0Uod0TFSUyotHsTLmO4pGo2BbY4PN/mWdZwHWMMVXAz0DzCN+beIqK7IjW1uQH0/vXdVz1wadwxhnQuTOsWgWPPAL77Zd0uxTFafTv3Jx6OXsbHvnG6vlfR2/nd4GBA21jmD17UmSxo0l/3U4RWotXUSIjGs3OlOsoHiPVgdr6+ceUBFsnkvfaDxAZC4wFKCgoCLRK3fHtrHjyyfH97DD4B/+fNmcq5OTA/PnQpk1SbVEUJ1PUIZ8ZYwfUxOr5xucFLGV2yy0weDA884wttaf4knDdTqhmpxAtm6cokRG1ZmcA8XCqy4D2Pv+3AzYGWadMRHKBpsC2CN8LgDFmKjAVbHxeHOzei9epLi5OulPt21hgYNUPNL/zRbj+enWoFSUAwSorBGzQUXCynYWaNMlWBGnSJAUWO5aE63ZCNTuFRNPES1Gynag0OwOIh1O9FOgmIp2A74FzgVF+68wDRmNj7s4E3jHGGBGZBzwvIg9gE166AZ/EwaboyM+34RZJalfuT81JN+ZuaNjQJiYqihIVtcRbBB56CI47zsZVT5uWOuOcR/rrdgrRsnmKEjuZeB3FHFPtibW7EngD+BJ40RizUkQmisjpntWmA809CS3XATd73rsSeBGbHPNfYFy4yh8JY8AAeO89cLvDr5sIfvwRnnsOLroImmfGNIiipJxjjrE3qdOnwwsvpNoax5Axuq0oiuIg4lKnOtkkpDzT00/D6NE2rvrII+P72ZFw1122TvbKldCjR/K3ryhpRFSlmCoq4KST7EzUhx+m5vr2wQkl9ZJNppXUUxQletKlhJ4/yS6plxl4Y6kXLIj5RzfqE2fnTpg82dqgDrWihMS/FNOEoT0p31UR/HrLy7PdSAsLYcwYWLYMXPEofKRkEun6g68o6YCvbue6hLP6tE/bZjCh0F8WL61bw2GHwRthGzqGxHvi3L/ga86ftpji0vLwb7rvPhv+MWlSTNtWlGzAtxRTRaWbCXNXhL/eWrWyDZQ+/RRefDG5BiuOp066rShKxOyj29WG55esz8hrTZ1qXwYPhg8+sCPHdSTq2otvv21r6Z51FvTtW+ftKkq24C3FlCPgcgluYyK73kaNgt694S9/gcrK5BmsOJ5MrZmrKE7Bq9veepyGzLzW1Kn25cwz7Y/to4/W+SN8f/DD1l6cOhUGDYL27e1otaIoYfGWYrrulO5MHNZrn+stv1EeUxauCTz6kZMDd98N336rlUCUfYhKtxVFiRqvbp/XryByzU5DNFHRn9NOg08+ge++i7qurTcmL79RXugYT4BNm6B7d+jXD+bOhUaN4mC8omQfvtfdxPkrQ7e9NQaOPx5Wr4Y1a1LSrVQTFZ2Dbxw1oDHVipIEotJsBxCNZutItT+3327blT/ySFRv843Jmzh/ZXhhvuUW2z750UfVoVaUGCjqkM+4E7pSvqsi/BS+CNx7L2zebJODlazFP44aYNwJXR35o64omURUmp1mqFPtz1FHwZAh9gf3118jfltUMXnFxfDUU7YhRdeucTBaUZSIp/AHDIBhw+Bvf7M30EpWonHUipJaMjHsSp3qQNx4I/zwg61dHYDi0vJaMUARnxzGwA03QIsWdrRaUZS44BtrHXYa8a674JdfYOLE5BmopIyYNFtRlIQQlWanCVqnOhDHH29HrB94AC691E4Ze/CvkfvcJf0BO+oRtl4uwGuvwcKFNrykadNk7I2iZA0Rt73t0QMuuwweftjWrx49OvHGKSkhkGZ7z5ERhe0Qz99M+EFXlHQj01qVq1MdCBG45BL7o7tqFfTsWfOS/5Th7JIy5pSURRZoX1VlR6kPPhjGjk3SziiKEpDJk22y4pgxcPjhcMQRqbZISQDBwjx8He0Rhe1SbKWiKJmAhn8EY+hQ+3fevH0W+08ZCgSNy6s15fjEE/Dll7YJRb16SdoRRVECkpdnG8E0bgx33JFqa5QEESjMI5ijHShMRFEUJVJ0pDoYbdrYEJB58/aJffbGAPmWYZpdUkZllXufuLxaU46/P4qiu++2SVLDh6dklxRF8aNZM7jqKtvNdMUK6NUr1RYpccZfs70ziXm5rn10O1SYiKIoSiSoUx2K00+HCRNg40brZHvwjwEKJNj+IyGbZr4M69bZigM+MdqKotQd3zrDdXaArrnGhoLcd5+tyqNkHP6aHcjRnrJwTa3Ra3WqFSW+xEWzHYw61aE480y47Tbo3x+eecYmMAYgUKD9sQ1/5evVH+GurGZbk3z6fP0qtG6to9SKEifiNrLYvDmcf751qCdPhvzME3qlNv667Q0Tqah0IyLkN8pLoXWKknlkw2xQTDHVInKAiLwpIqs9f2sdHRE5QkQ+FpGVIvKFiJzj89qTIvKdiHzmeTgrU+iQQ+D996F+fTjnHFuCy4da8XdVVbZk3pQpHHb0YTw8+y4emXcvzz93Mwct+5C3jx1G8cZfAmxIUZRoibbOcMh42bFjbV36555LkLXOIeN1OwShzoGiDvlMGNoTl0twG8Nt81Yw/uXlGl+tKHEirprtUGIdqb4ZeNsYc4+I3Oz5HLztpgAAFP5JREFU/ya/dXYBFxljVotIG6BYRN4wxmz3vH6DMWZWjHYkjgEDbL3qo4+G+++Hv/4VKitZ8dEX3DBrOesatyQvR3hjv6/pcMd4aNkS1q+H3/2Ol4ZczBPLNnHA/7az/6+/sKhdH8y0xRl5d6YoycY7suifz+BPcWk5s0vKmFVcRlV1kBGSwkL7mDoVxo3L9BCtzNftAEQySla+qwK3MbgNVFQbnl+yntklZarZihIHItVsgOeXrGfC3BW4jUmrUe1YnephwEDP86eARfiJszHmG5/nG0XkB6AlsJ10YcAAGwpy772wezfMmUOv1at5B3i7y1E0qtxDh/VfwAknQIMG1gF/8kk6b97FdxsX81WlG+P5qByN1VOUuBAsAc03Zg9s6bQ9Ptdg0HjZSy+FK66ATz6Bfv2SuCdJJzt0249Ao2T+54D3R997vhg0vlpR4kUwzYbauj1h7gqq3Fa1K9LoGozVqW5ljNkEYIzZJCIHhlpZRPoCecC3PovvFJEJwNvAzcaYPUHeOxYYC1BQUBCj2XXg4YetQ33vvdClC6V3PcDLb3zKmI9n8Wu9Bqy/7R4Kbr0BXHsjaoo61Oe5S/rXjJJVV4e/O1MUJXL842L9RyNHFrajomqvQy2E6J43ahT86U/w+OOZ7lQnRbdTrtl+RDJK5v3RV81WlMQQKActkG5Xu03N6y6RtLkGxRgTegWRt4CDArw0HnjKGNPMZ91yY0zAWwkRaY0dERltjFnss2wzVrCnAt8aY8L2De7Tp49ZtmxZuNUSQ1mZbTHeoAHFpeUs/WojfTu3oLBbKyB4ZmumZ7wqihOYsnAN9y/4GrexCSO92zXly807qa52k+MSzurTPnT3vDFjYOZMW/Fn//0TYqOIFBtj+iTkw/duw1G6nVLN9iGQDqtmK0pq8dVtAQb1aMX7q3+kotKNyyVMHNaLUf1Sd2MejWaHHak2xgwKsaEtItLaM9rRGvghyHr7A/8B/uIVZs9nb/I83SMi/wauj8TolNJub+etcKNkvjFAmdaKU1GciG8FBzew/PufyXUJ5/YtiKwV9dixtknTjBm2o2qaorodGNVsRXEe/Ts3J9clVFQbDPDuNz9y2+96Ur6rIu1uamPtqDgPGO15PhqY67+CiOQBLwNPG2Ne8nutteevAMOBFTHakxAizUCNNrNVUZT44p2+P6ZbC1wCbgPVbkObZg0jE+a+faF3bxsCkrlkvG6rZitK+lDUIZ+z+rTHmx5eXe2mfFcF407omlYONcTuVN8DnCwiq4GTPf8jIn1EZJpnnbOB44CLA5Rgek5ElgPLgRbApBjtiTvekYz7F3zN+dMWhxTpQO1wFUVJLkUd8rlm0MHk5bpwQXQ1h0VswmJxMZSUJNTOFJLRuq2arSjpx4jCdtSvVwfNdhhhY6qdSDLj83xjfXIEzulbQNtmDYNOSWgcnqI4g0hKMgW8XsvLbQfV0aPhX/+Ku13JiKl2GqrZiqKEI9Iyesm+ZuMaU53t+GaM57ikVq1bYJ8vV+PwFMUZ+NYcDlQWLWg8bX6+bfb07LO22k/TpincCyVaVLMVJT0Jp9ng/K6M6lSHwbeu4sbtu5nxyfqaL3x2SRlzSsoc++UqSjYTroRayLrFV15p25Y/9RRcfXUKrFfqimq2oqQnkZS9jKTefCpRpzoCvCMZ3s5s3i9cwNFfrqJkM4EaDfhOG4YU8D59bK3qRx6xDrYr1vQTJZmoZitK+hFJQ69oujKmAnWqo8D/Cwf2EWynfbmKku34Tu0HmjYM1t0LgKuuggsugDffhMGDU2C9Eiuq2YqSXkRS9jKkbqcYdaqjxP8Ld/KXqyjKXvynDWeXlIVMYOOss2yHxX/8Q53qNEY1W1HSF1/drqh0M/mtb7hm0MGMO6Frqk0LiDrVAYgms1STXBQlPQiXwFbrOs7Lsw1g7rgDvv0WunRJjeFKWFSzFSUz8W/o9eGan1i6bptj8yE0UNCPaGqcKoqSPnhDAa47pTsDux9IZSRNPy67DHJyElJaT4kPqtmKkrl4dbt3O1uFyemNmtSp9kM7bClK5lLUIZ/+nZuz6Osf8Fboz8kJEVvbpg0MHQrPPANVVUmzU4kc1WxFyXy+3LSj5nlIzU4x6lT7oR22FCWzWbx2K1Vu61ILcGZRu9DTiBdfDFu2wIIFSbFPiQ7VbEXJbKLW7BSiMdV+BCvpoihKZuBfkmlkYbvQbxgyBFq0gH//G047LTlGKhGjmq0omU3Ump1CtE25oihZR9Rtbm+4AR54wI5Wn3RSTNvWNuWKoijRkezW5L5om/I4kMovUFGUxBJ1BYi//hVeew3OPRc+/RTaOXekJFtRzVaUzCVdqvaoUx0Ap/eWVxQlyTRuDHPmQGEhXHEFzJsHIqm2SvGgmq0oihOIKVFRRA4QkTdFZLXnb0AVE5FqEfnM85jns7yTiCzxvH+miOTFYk+80GxyRckuikvLmbJwTehybN27w6RJMH8+zJyZPOPiTCbqtmq2omQXEWl2Coi1+sfNwNvGmG7A257/A7HbGHOE53G6z/J7gQc97y8HxsRoT1zQbHJFyR6iqnN89dXQt69tYf7TT8kzMr5knG6rZitK9uDk2vSxOtXDgKc8z58Chkf6RhER4ERgVl3en0h8m0R4pxGdelekKEpsRDXKmZMD06bB9u1w7bXJMzK+ZJxuB9JscO5olqIodcfJM1OxxlS3MsZsAjDGbBKRA4Os10BElgFVwD3GmFeA5sB2Y4y3o0IZ0DZGeyImXFKLb1C8xuspSubiX64p7Chn795wyy22ffnNN0PPnskxNH6kpW5Ho9ne9VW3FSXziFqzk0hYp1pE3gIOCvDS+Ci2U2CM2SginYF3RGQ5sCPAekHr+4nIWGAsQEFBQRSbrk20YhvorkjFWVEygzrVOR4/HgYPdqxD7QTdTqVmg+q2omQqTq5NH9apNsYMCvaaiGwRkdae0Y7WwA9BPmOj5+9aEVkEHAnMBpqJSK5n1KMdsDGEHVOBqWBrnoazOxTRiq2T74oURYmdqMs11a8PxxyTOINixAm6nUrNBtVtRclknFpiL9aY6nnAaM/z0cBc/xVEJF9E6nuetwCOAVYZ23VmIXBmqPcngmiTWoLF6ymKoqQhaafbdUlEVN1WFCXZxNRRUUSaAy8CBcB64CxjzDYR6QNcboy5RESOBh4D3FgnfrIxZrrn/Z2BF4ADgE+BC4wxe8JtNx7dubRRgKIoqSDVHRVToduq2YqipCvRaLa2KVcURUkiqXaqU4FqtqIo6Uo0mh1r+IeiKIqiKIqiZD3qVPugNU0VJXvR6z890e9NUbITJ177sdapTitCxeRpTVNFyV70+ncm4eKo9XtTlOzEqdd+1jjV4b4ArWmqKNmLXv/OI5IfTf3eFCU7ceq1nzXhH+HaWtalZJOiKJlBpNe/E6cbM5VIWhGrbitKduJUzc6akepAjQD8pxad2qFHUZTE4n/9A0xZuGYfLXDqdGOmEolmq24rSnbiVM3OGqc60BfgPdi5LuGsPu0ZUdiOcSd0TbGliqKkAq+TFkyInTrdmKlEqtlO7aymKEpicaJmZ034B9gvYNwJXWsd7Ipqw/NL1nP+tMW1pgh0uldRsos5JWXsqawddqChBslHNVtRlHA4SbOzZqTaH+/B3lPpxgCG2ncyOt2rKNlFcWk5Ly3bQE1LLBHyG+UBtUdOVQuSi2q2oij++Gu2uISN23dTXFqeEs3OqpFqX7wH+7x+BUHvZCJJlFEUJXNYvHYrVe69XWar3YaJ81fWjHr6jpwqyUU1W1EUf/w12xiY8cneWaxka3bWjlTD3nickYXtAt7JBEqUURQlc/EfDYXAo6E6Wp0aVLMVRfHF95oXEdzG1IqhTqZmizEm/FoOo0+fPmbZsmV1em+0B1d/QBUluyguLWd2SRmzisuorrbOmTeMIB7hBSJSbIzpkyDzHUksmg3R6bBqtqJkF95rPr9RHhPnr6y5qX7ukv4ASdXsrBqpDvWDGEyINbNcUbIL/9HQ/EZ5NWEEvuEFeyrdzC4pU31IMMF0WzVbURTY95rvflATZpeUIZ7X9klwrnQz+a1vuGbQwQnTiJicahE5AJgJdATWAWcbY8r91jkBeNBn0SHAucaYV0TkSeB44GfPaxcbYz6LxaZQBCuvosktiqL449UAX22YMLQnuS6hotpggFnFZYz0lHVLFzJBtyH20SdFUTKTOSVlVFTZQY8JQ3uSl+uiotKNG/hwzU8sXbctYZoRa6LizcDbxphuwNue//fBGLPQGHOEMeYI4ERgF7DAZ5UbvK8nUpgheHkVTW5RFCUQ/tpQvquCs/q0rxkFqa5OS71Ie91WzVYUJRCBNPu5S/pzTLcWuISEa0as4R/DgIGe508Bi4CbQqx/JvC6MWZXjNutE8HKq2hyi6IogQimDbNLytJZLzJCt1WzFUXxJ5BmF3XI55pBB7N03baEa0ZMiYoist0Y08zn/3JjTNDxdBF5B3jAGDPf8/+TwABgD54RE2PMnnDbjTXpJRCa3KIoSiACaUMsepHqRMVU6LZqtqIoySKYNtRVM6LR7LBOtYi8BRwU4KXxwFORirOItAa+ANoYYyp9lm0G8oCpwLfGmIlB3j8WGAtQUFBQVFpaGmbXFEVRnEcynGon6LZqtqIomUBcq38YYwaF2NAWEWltjNnkEdofQnzU2cDLXmH2fPYmz9M9IvJv4PoQdkzFCjh9+vRJvzqAiqIoScIJuq2arShKthFrouI8YLTn+Whgboh1zwNm+C7wCDoiIsBwYEWM9iiKoiihUd1WFEVJALE61fcAJ4vIauBkz/+ISB8RmeZdSUQ6Au2Bd/3e/5yILAeWAy2ASTHaoyiKEneKS8uZsnBNTbvyNEd1W1GUjCZVmh1T9Q9jzFbgpADLlwGX+Py/DmgbYL0TY9m+oihKosm0Ovaq24qiZDKp1OxYR6oVRVEyGq2JrCiKkj6kUrPVqVYURQlBsKZRiqIoivNIpWbH2vxFURQlownWfERRFEVxHqnUbHWqFUVRwlDUIV+daUVRlDQhVZqt4R+KoiiKoiiKEiPqVCuKoiiKoihKjKhTrSiKoiiKoigxok61oiiKoiiKosSIOtWKoiiKoiiKEiNijEm1DVEjIj8CpXV4awvgpzibUxecYgeoLcFwii1OsQPUlmBEa0sHY0zLRBnjRDJAs8E5tjjFDlBbguEUW5xiB6S3LRFrdlo61XVFRJYZY/qoHXtRWwLjFFucYgeoLcFwki2ZhpOOrVNscYodoLYEwym2OMUOyB5bNPxDURRFURRFUWJEnWpFURRFURRFiZFsc6qnptoAD06xA9SWYDjFFqfYAWpLMJxkS6bhpGPrFFucYgeoLcFwii1OsQOyxJasiqlWFEVRFEVRlESQbSPViqIoiqIoihJ3ssKpFpFTReRrEVkjIjcnedvtRWShiHwpIitF5I+e5beJyPci8pnncVqS7FknIss921zmWXaAiLwpIqs9f/MTbEN3n/3+TER2iMg1yTomIvKEiPwgIit8lgU8BmJ52HPufCEihUmw5T4R+cqzvZdFpJlneUcR2e1zfP6VBFuCficicovnuHwtIoOTYMtMHzvWichnnuUJOy4hrt+UnC/Zgmr2PvakXLM921TdDm6HarZqtsUYk9EPIAf4FugM5AGfAz2SuP3WQKHneRPgG6AHcBtwfQqOxzqghd+yvwE3e57fDNyb5O9nM9AhWccEOA4oBFaEOwbAacDrgAD9gSVJsOUUINfz/F4fWzr6rpek4xLwO/Gcw58D9YFOnmssJ5G2+L1+PzAh0cclxPWbkvMlGx6q2bXscZRm+3xHWanbqtmR2+L3elZodjaMVPcF1hhj1hpjKoAXgGHJ2rgxZpMxpsTzfCfwJdA2WduPkGHAU57nTwHDk7jtk4BvjTF1aQxRJ4wx7wHb/BYHOwbDgKeNZTHQTERaJ9IWY8wCY0yV59/FQLt4bS9aW0IwDHjBGLPHGPMdsAZ7rSXcFhER4GxgRry2F8KOYNdvSs6XLEE1Ozyp1GzIYt1WzY7elmzS7GxwqtsCG3z+LyNFAikiHYEjgSWeRVd6phueSMb0nQcDLBCRYhEZ61nWyhizCewJCRyYJFsAzmXfCy0VxwSCH4NUnz9/wN5Fe+kkIp+KyLsicmySbAj0naTyuBwLbDHGrPZZlvDj4nf9OvV8yQQccwxVs4Oiuh0c1ezaZI1mZ4NTLQGWJb3kiYg0BmYD1xhjdgCPAl2AI4BN2KmRZHCMMaYQGAKME5HjkrTdWohIHnA68JJnUaqOSShSdv6IyHigCnjOs2gTUGCMORK4DnheRPZPsBnBvpNUXlfnse8PesKPS4DrN+iqAZZpiaXocMQxVM0OjOp2iI2qZgcjazQ7G5zqMqC9z//tgI3JNEBE6mG/3OeMMXMAjDFbjDHVxhg38DhxnIYJhTFmo+fvD8DLnu1u8U53eP7+kAxbsD8SJcaYLR6bUnJMPAQ7Bik5f0RkNDAUON94Ar8803ZbPc+LsTFxByfSjhDfSaqOSy4wApjpY2NCj0ug6xeHnS8ZRsqPoWp2SFS3A6CaHZhs0+xscKqXAt1EpJPnDvtcYF6yNu6JJZoOfGmMecBnuW/MzhnACv/3JsCW/USkifc5NrliBfZ4jPasNhqYm2hbPOxz95qKY+JDsGMwD7jIkyHcH/jZO4WUKETkVOAm4HRjzC6f5S1FJMfzvDPQDVibYFuCfSfzgHNFpL6IdPLY8kkibfEwCPjKGFPmY2PCjkuw6xcHnS8ZiGr23m06TbNBdbsWqtkhyS7NNgnKSnXSA5vd+Q32bmh8krf9G+xUwhfAZ57HacAzwHLP8nlA6yTY0hmb/fs5sNJ7LIDmwNvAas/fA5JgSyNgK9DUZ1lSjgn2B2ETUIm9Sx0T7Bhgp4ameM6d5UCfJNiyBhvj5T1f/uVZd6Tne/scKAF+lwRbgn4nwHjPcfkaGJJoWzzLnwQu91s3YcclxPWbkvMlWx6q2TW2OEazPdvNet1WzY7cFs/yrNJs7aioKIqiKIqiKDGSDeEfiqIoiqIoipJQ1KlWFEVRFEVRlBhRp1pRFEVRFEVRYkSdakVRFEVRFEWJEXWqFUVRFEVRFCVG1KlWFEVRFEVRlBhRp1pRFEVRFEVRYkSdakVRFEVRFEWJkf8HSHGqbihpX7IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mom(mom2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Debiasing is here to correct the wrong information we may have in the very first batch. The debias term corresponds to the sum of the coefficient in our moving average. At the time step i, our average is:\n",
    "\n",
    "$\\begin{align*}\n",
    "avg_{i} &= \\beta\\ avg_{i-1} + (1-\\beta)\\ v_{i} = \\beta\\ (\\beta\\ avg_{i-2} + (1-\\beta)\\ v_{i-1}) + (1-\\beta)\\ v_{i} \\\\\n",
    "&= \\beta^{2}\\ avg_{i-2} + (1-\\beta)\\ \\beta\\ v_{i-1} + (1-\\beta)\\ v_{i} \\\\\n",
    "&= \\beta^{3}\\ avg_{i-3} + (1-\\beta)\\ \\beta^{2}\\ v_{i-2} + (1-\\beta)\\ \\beta\\ v_{i-1} + (1-\\beta)\\ v_{i} \\\\\n",
    "&\\vdots \\\\\n",
    "&= (1-\\beta)\\ \\beta^{i}\\ v_{0} + (1-\\beta)\\ \\beta^{i-1}\\ v_{1} + \\cdots + (1-\\beta)\\ \\beta^{2}\\ v_{i-2} + (1-\\beta)\\ \\beta\\  v_{i-1} + (1-\\beta)\\ v_{i}\n",
    "\\end{align*}$\n",
    "\n",
    "and so the sum of the coefficients is\n",
    "\n",
    "$\\begin{align*}\n",
    "S &=(1-\\beta)\\ \\beta^{i} + (1-\\beta)\\ \\beta^{i-1} + \\cdots + (1-\\beta)\\ \\beta^{2} + (1-\\beta)\\ \\beta + (1-\\beta) \\\\\n",
    "&= (\\beta^{i} - \\beta^{i+1}) + (\\beta^{i-1} - \\beta^{i}) + \\cdots + (\\beta^{2} - \\beta^{3}) + (\\beta - \\beta^{2}) + (1-\\beta) \\\\\n",
    "&= 1 - \\beta^{i+1}\n",
    "\\end{align*}$\n",
    "\n",
    "since all the other terms cancel out each other.\n",
    "\n",
    "By dividing by this term, we make our moving average a true average (in the sense that all the coefficients we used for the average sum up to 1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def mom3(avg, beta, yi, i):\n",
    "    if avg is None: avg=0\n",
    "    avg = lin_comb(avg, yi, beta)\n",
    "    return avg, avg/(1-beta**(i+1))\n",
    "plot_mom(mom3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adam and friends"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In Adam, we use the gradient averages but with dampening (not like in SGD with momentum), so let's add this to the `AverageGrad` class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=5889)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class AverageGrad(Stat):\n",
    "    _defaults = dict(mom=0.9)\n",
    "    \n",
    "    def __init__(self, dampening:bool=False): self.dampening=dampening\n",
    "    def init_state(self, p): return {'grad_avg': torch.zeros_like(p.grad.data)}\n",
    "    def update(self, p, state, mom, **kwargs):\n",
    "        state['mom_damp'] = 1-mom if self.dampening else 1.\n",
    "        state['grad_avg'].mul_(mom).add_(state['mom_damp'], p.grad.data)\n",
    "        return state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to track the moving average of the gradients squared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class AverageSqrGrad(Stat):\n",
    "    _defaults = dict(sqr_mom=0.99)\n",
    "    \n",
    "    def __init__(self, dampening:bool=True): self.dampening=dampening\n",
    "    def init_state(self, p): return {'sqr_avg': torch.zeros_like(p.grad.data)}\n",
    "    def update(self, p, state, sqr_mom, **kwargs):\n",
    "        state['sqr_damp'] = 1-sqr_mom if self.dampening else 1.\n",
    "        state['sqr_avg'].mul_(sqr_mom).addcmul_(state['sqr_damp'], p.grad.data, p.grad.data)\n",
    "        return state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need the number of steps done during training for the debiasing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "class StepCount(Stat):\n",
    "    def init_state(self, p): return {'step': 0}\n",
    "    def update(self, p, state, **kwargs):\n",
    "        state['step'] += 1\n",
    "        return state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This helper function computes the debias term. If we dampening, `damp = 1 - mom` and we get the same result as before. If we don't use dampening, (`damp = 1`) we will need to divide by `1 - mom` because that term is missing everywhere."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def debias(mom, damp, step): return damp * (1 - mom**step) / (1-mom)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then the Adam step is just the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def adam_step(p, lr, mom, mom_damp, step, sqr_mom, sqr_damp, grad_avg, sqr_avg, eps, **kwargs):\n",
    "    debias1 = debias(mom,     mom_damp, step)\n",
    "    debias2 = debias(sqr_mom, sqr_damp, step)\n",
    "    p.data.addcdiv_(-lr / debias1, grad_avg, (sqr_avg/debias2).sqrt() + eps)\n",
    "    return p\n",
    "adam_step._defaults = dict(eps=1e-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#export\n",
    "def adam_opt(xtra_step=None, **kwargs):\n",
    "    return partial(StatefulOptimizer, steppers=[adam_step,weight_decay]+listify(xtra_step),\n",
    "                   stats=[AverageGrad(dampening=True), AverageSqrGrad(), StepCount()], **kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn,run = get_learn_run(nfs, data, 0.001, conv_layer, cbs=cbfs, opt_func=adam_opt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [1.7606317618659841, tensor(0.3931, device='cuda:0')]\n",
      "valid: [1.4439287109375, tensor(0.5320, device='cuda:0')]\n",
      "train: [1.246226745482395, tensor(0.5872, device='cuda:0')]\n",
      "valid: [1.113287353515625, tensor(0.6260, device='cuda:0')]\n",
      "train: [0.9583190188992943, tensor(0.6872, device='cuda:0')]\n",
      "valid: [0.9859400634765625, tensor(0.6720, device='cuda:0')]\n"
     ]
    }
   ],
   "source": [
    "run.fit(3, learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LAMB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Jump_to lesson 11 video](https://course19.fast.ai/videos/?lesson=11&t=6038)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's then super easy to implement a new optimizer. This is LAMB from a [very recent paper](https://arxiv.org/pdf/1904.00962.pdf):\n",
    "\n",
    "$\\begin{align}\n",
    "g_{t}^{l} &= \\nabla L(w_{t-1}^{l}, x_{t}) \\\\\n",
    "m_{t}^{l} &= \\beta_{1} m_{t-1}^{l} + (1-\\beta_{1}) g_{t}^{l} \\\\\n",
    "v_{t}^{l} &= \\beta_{2} v_{t-1}^{l} + (1-\\beta_{2}) g_{t}^{l} \\odot g_{t}^{l} \\\\\n",
    "m_{t}^{l} &= m_{t}^{l} / (1 - \\beta_{1}^{t}) \\\\\n",
    "v_{t}^{l} &= v_{t}^{l} / (1 - \\beta_{2}^{t}) \\\\\n",
    "r_{1} &= \\|w_{t-1}^{l}\\|_{2} \\\\\n",
    "s_{t}^{l} &= \\frac{m_{t}^{l}}{\\sqrt{v_{t}^{l} + \\epsilon}} + \\lambda w_{t-1}^{l} \\\\ \n",
    "r_{2} &= \\| s_{t}^{l} \\|_{2} \\\\\n",
    "\\eta^{l} &= \\eta * r_{1}/r_{2} \\\\ \n",
    "w_{t}^{l} &= w_{t}^{l-1} - \\eta_{l} * s_{t}^{l} \\\\\n",
    "\\end{align}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lamb_step(p, lr, mom, mom_damp, step, sqr_mom, sqr_damp, grad_avg, sqr_avg, eps, wd, **kwargs):\n",
    "    debias1 = debias(mom,     mom_damp, step)\n",
    "    debias2 = debias(sqr_mom, sqr_damp, step)\n",
    "    r1 = p.data.pow(2).mean().sqrt()\n",
    "    step = (grad_avg/debias1) / ((sqr_avg/debias2).sqrt()+eps) + wd*p.data\n",
    "    r2 = step.pow(2).mean().sqrt()\n",
    "    p.data.add_(-lr * min(r1/r2,10), step)\n",
    "    return p\n",
    "lamb_step._defaults = dict(eps=1e-6, wd=0.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lamb = partial(StatefulOptimizer, steppers=lamb_step, stats=[AverageGrad(dampening=True), AverageSqrGrad(), StepCount()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn,run = get_learn_run(nfs, data, 0.003, conv_layer, cbs=cbfs, opt_func=lamb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train: [1.8728660132619823, tensor(0.3419, device='cuda:0')]\n",
      "valid: [1.484662841796875, tensor(0.4860, device='cuda:0')]\n",
      "train: [1.3553239394291918, tensor(0.5506, device='cuda:0')]\n",
      "valid: [1.537447021484375, tensor(0.4840, device='cuda:0')]\n",
      "train: [1.0624497101364976, tensor(0.6537, device='cuda:0')]\n",
      "valid: [1.11101806640625, tensor(0.6460, device='cuda:0')]\n"
     ]
    }
   ],
   "source": [
    "run.fit(3, learn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Other recent variants of optimizers:\n",
    "- [Large Batch Training of Convolutional Networks](https://arxiv.org/abs/1708.03888) (LARS also uses weight statistics, not just gradient statistics. Can you add that to this class?)\n",
    "- [Adafactor: Adaptive Learning Rates with Sublinear Memory Cost](https://arxiv.org/abs/1804.04235) (Adafactor combines stats over multiple sets of axes)\n",
    "- [Adaptive Gradient Methods with Dynamic Bound of Learning Rate](https://arxiv.org/abs/1902.09843)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python notebook2script.py 09_optimizers.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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